specialized model learning for optimization



Kenneth Baker R. Optimization Modeling with Spreadsheets Kenneth Baker R. Optimization Modeling with Spreadsheets Новинка

Kenneth Baker R. Optimization Modeling with Spreadsheets

An accessible introduction to optimization analysis using spreadsheets Updated and revised, Optimization Modeling with Spreadsheets, Third Edition emphasizes model building skills in optimization analysis. By emphasizing both spreadsheet modeling and optimization tools in the freely available Microsoft® Office Excel® Solver, the book illustrates how to find solutions to real-world optimization problems without needing additional specialized software. The Third Edition includes many practical applications of optimization models as well as a systematic framework that illuminates the common structures found in many successful models. With focused coverage on linear programming, nonlinear programming, integer programming, and heuristic programming, Optimization Modeling with Spreadsheets, Third Edition features: An emphasis on model building using Excel Solver as well as appendices with additional instructions on more advanced packages such as Analytic Solver Platform and OpenSolver Additional space devoted to formulation principles and model building as opposed to algorithms New end-of-chapter homework exercises specifically for novice model builders Presentation of the Sensitivity Toolkit for sensitivity analysis with Excel Solver Classification of problem types to help readers see the broader possibilities for application Specific chapters devoted to network models and data envelopment analysis A companion website with interactive spreadsheets and supplementary homework exercises for additional practice Optimization Modeling with Spreadsheets, Third Edition is an excellent textbook for upper-undergraduate and graduate-level courses that include deterministic models, optimization, spreadsheet modeling, quantitative methods, engineering management, engineering modeling, operations research, and management science. The book is an ideal reference for readers wishing to advance their knowledge of Excel and modeling and is also a useful guide for MBA students and modeling practitioners in business and non-profit sectors interested in spreadsheet optimization.
Ali Akansu N. Financial Signal Processing and Machine Learning Ali Akansu N. Financial Signal Processing and Machine Learning Новинка

Ali Akansu N. Financial Signal Processing and Machine Learning

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.
Aleksandar Vakanski Robot Learning by Visual Observation Aleksandar Vakanski Robot Learning by Visual Observation Новинка

Aleksandar Vakanski Robot Learning by Visual Observation

This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstraction Discusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problem Focuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regression Concentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert
Parag Kulkarni Reinforcement and Systemic Machine Learning for Decision Making Parag Kulkarni Reinforcement and Systemic Machine Learning for Decision Making Новинка

Parag Kulkarni Reinforcement and Systemic Machine Learning for Decision Making

Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making. Chapters include: Introduction to Reinforcement and Systemic Machine Learning Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning Systemic Machine Learning and Model Inference and Information Integration Adaptive Learning Incremental Learning and Knowledge Representation Knowledge Augmentation: A Machine Learning Perspective Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.
Bertram K. C. Chan Applied Probabilistic Calculus for Financial Engineering. An Introduction Using R Bertram K. C. Chan Applied Probabilistic Calculus for Financial Engineering. An Introduction Using R Новинка

Bertram K. C. Chan Applied Probabilistic Calculus for Financial Engineering. An Introduction Using R

10357.59 руб. Найти похожее
Illustrates how R may be used successfully to solve problems in quantitative finance Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R provides R recipes for asset allocation and portfolio optimization problems. It begins by introducing all the necessary probabilistic and statistical foundations, before moving on to topics related to asset allocation and portfolio optimization with R codes illustrated for various examples. This clear and concise book covers financial engineering, using R in data analysis, and univariate, bivariate, and multivariate data analysis. It examines probabilistic calculus for modeling financial engineering—walking the reader through building an effective financial model from the Geometric Brownian Motion (GBM) Model via probabilistic calculus, while also covering Ito Calculus. Classical mathematical models in financial engineering and modern portfolio theory are discussed—along with the Two Mutual Fund Theorem and The Sharpe Ratio. The book also looks at R as a calculator and using R in data analysis in financial engineering. Additionally, it covers asset allocation using R, financial risk modeling and portfolio optimization using R, global and local optimal values, locating functional maxima and minima, and portfolio optimization by performance analytics in CRAN. Covers optimization methodologies in probabilistic calculus for financial engineering Answers the question: What does a «Random Walk» Financial Theory look like? Covers the GBM Model and the Random Walk Model Examines modern theories of portfolio optimization, including The Markowitz Model of Modern Portfolio Theory (MPT), The Black-Litterman Model, and The Black-Scholes Option Pricing Model Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R s an ideal reference for professionals and students in economics, econometrics, and finance, as well as for financial investment quants and financial engineers.
Dan Simon Evolutionary Optimization Algorithms Dan Simon Evolutionary Optimization Algorithms Новинка

Dan Simon Evolutionary Optimization Algorithms

10203.28 руб. Найти похожее
A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear—but theoretically rigorous—understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs—including opposition-based learning, artificial fish swarms, bacterial foraging, and many others— and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.
Stanislaw H. Zak An Introduction to Optimization Stanislaw H. Zak An Introduction to Optimization Новинка

Stanislaw H. Zak An Introduction to Optimization

Praise for the Third Edition «. . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail.» —MAA Reviews Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus. This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also offers: A new chapter on integer programming Expanded coverage of one-dimensional methods Updated and expanded sections on linear matrix inequalities Numerous new exercises at the end of each chapter MATLAB exercises and drill problems to reinforce the discussed theory and algorithms Numerous diagrams and figures that complement the written presentation of key concepts MATLAB M-files for implementation of the discussed theory and algorithms (available via the book's website) Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods. In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.
Ryzhov Ilya O. Optimal Learning Ryzhov Ilya O. Optimal Learning Новинка

Ryzhov Ilya O. Optimal Learning

Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduc­tion to learning and a variety of policies for learning.
John Nash C. Nonlinear Parameter Optimization Using R Tools John Nash C. Nonlinear Parameter Optimization Using R Tools Новинка

John Nash C. Nonlinear Parameter Optimization Using R Tools

Nonlinear Parameter Optimization Using R John C. Nash, Telfer School of Management, University of Ottawa, Canada A systematic and comprehensive treatment of optimization software using R In recent decades, optimization techniques have been streamlined by computational and artificial intelligence methods to analyze more variables, especially under non–linear, multivariable conditions, more quickly than ever before. Optimization is an important tool for decision science and for the analysis of physical systems used in engineering. Nonlinear Parameter Optimization with R explores the principal tools available in R for function minimization, optimization, and nonlinear parameter determination and features numerous examples throughout. Nonlinear Parameter Optimization with R: Provides a comprehensive treatment of optimization techniques Examines optimization problems that arise in statistics and how to solve them using R Enables researchers and practitioners to solve parameter determination problems Presents traditional methods as well as recent developments in R Is supported by an accompanying website featuring R code, examples and datasets Researchers and practitioners who have to solve parameter determination problems who are users of R but are novices in the field optimization or function minimization will benefit from this book. It will also be useful for scientists building and estimating nonlinear models in various fields such as hydrology, sports forecasting, ecology, chemical engineering, pharmaco-kinetics, agriculture, economics and statistics.
Ibrahim Dincer Optimization of Energy Systems Ibrahim Dincer Optimization of Energy Systems Новинка

Ibrahim Dincer Optimization of Energy Systems

10740.45 руб. Найти похожее
An essential resource for optimizing energy systems to enhance design capability, performance and sustainability Optimization of Energy Systems comprehensively describes the thermodynamic modelling, analysis and optimization of numerous types of energy systems in various applications. It provides a new understanding of the system and the process of defining proper objective functions for determination of the most suitable design parameters for achieving enhanced efficiency, cost effectiveness and sustainability. Beginning with a general summary of thermodynamics, optimization techniques and optimization methods for thermal components, the book goes on to describe how to determine the most appropriate design parameters for more complex energy systems using various optimization methods. The results of each chapter provide potential tools for design, analysis, performance improvement, and greenhouse gas emissions reduction. Key features: Comprehensive coverage of the modelling, analysis and optimization of many energy systems for a variety of applications. Examples, practical applications and case studies to put theory into practice. Study problems at the end of each chapter that foster critical thinking and skill development. Written in an easy-to-follow style, starting with simple systems and moving to advanced energy systems and their complexities. A unique resource for understanding cutting-edge research in the thermodynamic analysis and optimization of a wide range of energy systems, Optimization of Energy Systems is suitable for graduate and senior undergraduate students, researchers, engineers, practitioners, and scientists in the area of energy systems.
Xincheng Zhang LTE Optimization Engineering Handbook Xincheng Zhang LTE Optimization Engineering Handbook Новинка

Xincheng Zhang LTE Optimization Engineering Handbook

11891.21 руб. Найти похожее
A comprehensive resource containing the operating principles and key insights of LTE networks performance optimization LTE Optimization Engineering Handbook is a comprehensive reference that describes the most current technologies and optimization principles for LTE networks. The text offers an introduction to the basics of LTE architecture, services and technologies and includes details on the key principles and methods of LTE optimization and its parameters. In addition, the author clarifies different optimization aspects such as wireless channel optimization, data optimization, CSFB, VoLTE, and video optimization. With the ubiquitous usage and increased development of mobile networks and smart devices, LTE is the 4G network that will be the only mainstream technology in the current mobile communication system and in the near future. Designed for use by researchers, engineers and operators working in the field of mobile communications and written by a noted engineer and experienced researcher, the LTE Optimization Engineering Handbook provides an essential guide that: Discusses the latest optimization engineering technologies of LTE networks and explores their implementation Features the latest and most industrially relevant applications, such as VoLTE and HetNets Includes a wealth of detailed scenarios and optimization real-world case studies Professionals in the field will find the LTE Optimization Engineering Handbook to be their go-to reference that includes a thorough and complete examination of LTE networks, their operating principles, and the most current information to performance optimization.
Ali Zomorrodi R. Optimization Methods in Metabolic Networks Ali Zomorrodi R. Optimization Methods in Metabolic Networks Новинка

Ali Zomorrodi R. Optimization Methods in Metabolic Networks

Provides a tutorial on the computational tools that use mathematical optimization concepts and representations for the curation, analysis and redesign of metabolic networks Organizes, for the first time, the fundamentals of mathematical optimization in the context of metabolic network analysis Reviews the fundamentals of different classes of optimization problems including LP, MILP, MLP and MINLP Explains the most efficient ways of formulating a biological problem using mathematical optimization Reviews a variety of relevant problems in metabolic network curation, analysis and redesign with an emphasis on details of optimization formulations Provides a detailed treatment of bilevel optimization techniques for computational strain design and other relevant problems
Bouchaib Radi Dynamics of Large Structures and Inverse Problems Bouchaib Radi Dynamics of Large Structures and Inverse Problems Новинка

Bouchaib Radi Dynamics of Large Structures and Inverse Problems

This book deals with the various aspects of stochastic dynamics, the resolution of large mechanical systems, and inverse problems. It integrates the most recent ideas from research and industry in the field of stochastic dynamics and optimization in structural mechanics over 11 chapters. These chapters provide an update on the various tools for dealing with uncertainties, stochastic dynamics, reliability and optimization of systems. The optimization–reliability coupling in structures dynamics is approached in order to take into account the uncertainties in the modeling and the resolution of the problems encountered. Accompanied by detailed examples of uncertainties, optimization, reliability, and model reduction, this book presents the newest design tools. It is intended for students and engineers and is a valuable support for practicing engineers and teacher-researchers.
Jizhong Zhu Optimization of Power System Operation Jizhong Zhu Optimization of Power System Operation Новинка

Jizhong Zhu Optimization of Power System Operation

10740.45 руб. Найти похожее
Optimization of Power System Operation, 2nd Edition, offers a practical, hands-on guide to theoretical developments and to the application of advanced optimization methods to realistic electric power engineering problems. The book includes: New chapter on Application of Renewable Energy, and a new chapter on Operation of Smart Grid New topics include wheeling model, multi-area wheeling, and the total transfer capability computation in multiple areas Continues to provide engineers and academics with a complete picture of the optimization of techniques used in modern power system operation
Clark Ruth C. Scenario-based e-Learning. Evidence-Based Guidelines for Online Workforce Learning Clark Ruth C. Scenario-based e-Learning. Evidence-Based Guidelines for Online Workforce Learning Новинка

Clark Ruth C. Scenario-based e-Learning. Evidence-Based Guidelines for Online Workforce Learning

Scenario-Based e-Learning Scenario-Based e-Learning offers a new instructional design approach that can accelerate expertise, build critical thinking skills, and promote transfer of learning. This book focuses on the what, when, and how of scenario-based e-learning for workforce learning. Throughout the book, Clark defines and demystifies scenario-based e-learning by offering a practical design model illustrated with examples from veterinary science, automotive troubleshooting, sales and loan analysis among other industries. Filled with helpful guidelines and a wealth of illustrative screen shots, this book offers you the information needed to: Identify the benefits of a SBeL design for learners and learning outcomes Determine when SBeL might be appropriate for your needs Identify specific outcomes of SBeL relevant to common organizational goals Classify specific instructional goals into one or more learning domains Apply a design model to present content in a task-centered context Evaluate outcomes from SBeL lessons Identify tacit expert knowledge using cognitive task analysis techniques Make a business case for SBeL in your organization Praise for Scenario-Based e-Learning «Clark has done it again—with her uncanny ability to make complex ideas accessible to practitioners, the guidelines in this book provide an important resource for you to build your own online, problem-centered instructional strategies.» —M. David Merrill, professor emeritus at Utah State University; author, First Principles of Instruction «Clark's wonderful book provides a solid explanation of the how, what, and why of scenario-based e-learning. The tools, techniques, and resources in this book provide a roadmap for creating engaging, informative scenarios that lead to tangible, measurable learning outcomes. If you want to design more engaging e-learning, you need to read this book.» —Karl M. Kapp, Professor of Instructional Technology, Bloomsburg University; author, The Gamification of Learning and Instruction
Rich Page Website Optimization. An Hour a Day Rich Page Website Optimization. An Hour a Day Новинка

Rich Page Website Optimization. An Hour a Day

Step-by-step instructions for executing a website testing and optimization plan Website optimization is can be an overwhelming endeavor due to the fact that it encompasses so many strategic and technical issues. However, this hands-on, task-based book demystifies this potentially intimidating topic by offering smart, practical, and tested instructions for developing, implementing, managing, and tracking website optimization efforts. After you learn how to establish an optimization framework, you then dive into learning how to develop a plan, test appropriately and accurately, interpret the results, and optimize in order to maximize conversion rates and improve profits. Zeroes in on fundamentals such as understanding key metrics, choosing analytics tools, researching visitors and their onsite behavior, and crafting a plan for what to test and optimize Walks you through testing and optimizing specific web pages including the homepage, entry and exit pages, product and pricing pages, as well as the shopping cart and check-out process Guides you through important optimization areas such as optimizing text and images Addresses advanced topics including paid search optimization, Facebook fan page optimization, rich media, and more Includes a companion website that features expanded examples, additional resources, tool reviews, and other related information Full of interesting case studies and helpful examples drawn from the author's own experience, Website Optimization: An Hour a Day is the complete solution for anyone who wants to get the best possible results from their web page.
Christian Blum Metaheuristics for String Problems in Bio-informatics Christian Blum Metaheuristics for String Problems in Bio-informatics Новинка

Christian Blum Metaheuristics for String Problems in Bio-informatics

10357.59 руб. Найти похожее
So-called string problems are abundant in bioinformatics and computational biology. New optimization problems dealing with DNA or protein sequences are constantly arising and researchers are highly in need of efficient optimization techniques for solving them. One obstacle for optimization practitioners is the atypical nature of these problems which require an interdisciplinary approach in order to solve them efficiently and accurately.
Subir Chowdhury Robust Optimization. World's Best Practices for Developing Winning Vehicles Subir Chowdhury Robust Optimization. World's Best Practices for Developing Winning Vehicles Новинка

Subir Chowdhury Robust Optimization. World's Best Practices for Developing Winning Vehicles

Robust Optimization is a method to improve robustness using low-cost variations of a single, conceptual design. The benefits of Robust Optimization include faster product development cycles; faster launch cycles; fewer manufacturing problems; fewer field problems; lower-cost, higher performing products and processes; and lower warranty costs. All these benefits can be realized if engineering and product development leadership of automotive and manufacturing organizations leverage the power of using Robust Optimization as a competitive weapon. Written by world renowned authors, Robust Optimization: World’s Best Practices for Developing Winning Vehicles, is a ground breaking book whichintroduces the technical management strategy of Robust Optimization. The authors discuss what the strategy entails, 8 steps for Robust Optimization and Robust Assessment, and how to lead it in a technical organization with an implementation strategy. Robust Optimization is defined and it is demonstrated how the techniques can be applied to manufacturing organizations, especially those with automotive industry applications, so that Robust Optimization creates the flexibility that minimizes product development cost, reduces product time-to-market, and increases overall productivity. Key features: Presents best practices from around the globe on Robust Optimization that can be applied in any manufacturing and automotive organization in the world Includes 19 successfully implemented best case studies from automotive original equipment manufacturers and suppliers Provides manufacturing industries with proven techniques to become more competitive in the global market Provides clarity concerning the common misinterpretations on Robust Optimization Robust Optimization: World’s Best Practices for Developing Winning Vehicles is a must-have book for engineers and managers who are working on design, product, manufacturing, mechanical, electrical, process, quality area; all levels of management especially in product development area, research and development personnel and consultants. It also serves as an excellent reference for students and teachers in engineering.
Kathleen King P. Technology and Innovation in Adult Learning Kathleen King P. Technology and Innovation in Adult Learning Новинка

Kathleen King P. Technology and Innovation in Adult Learning

A comprehensive exploration of technology's role in adult learning Technology and Innovation in Adult Learning introduces educators and students to the intersection of adult learning and the growing technological revolution. Written by an internationally recognized expert in the field, this book explores the theory, research, and practice driving innovation in both adult learning and learning technology, and illuminates a powerful approach to recognize and leverage these opportunities. Building on current trends and research in technology and its use, each chapter illustrates the need, opportunities, and examples of current and future technologies that scaffold adult learning, and provides comprehensive coverage of both current and emerging challenges. Many adult learning faculty, practitioners, and students realize that technology presents a growing and ever-present set of issues, yet few feel confident in identifying the opportunities that arise with each step forward. This book clarifies the interplay between adult learning and learning technology, and characterizes the cyclic exchange of information and opportunities that link these fields now and in the future. Understand the critical issues currently affecting adult learning Learn how technology is presenting both opportunities and challenges for the teaching and learning of adults in different contexts Examine recent research on learning technology for adult learners Discover how technological innovation can be applied now and how it will continue to shape the future of learning Adult learning is on the rise, and there is no mistaking technology's role; whether they're learning with or about technology, today's adult learners come with unique sets of needs and skills that demand specialized approaches. Traditional pedagogical techniques don't transfer directly, and learning technology requires its own unique approach to development and use. Technology and Innovation in Adult Learning equips practitioners to further adult learning and shape the future of the field, while providing a rich perspective for classroom inquiry and research.
Michael Bowles Machine Learning in Python. Essential Techniques for Predictive Analysis Michael Bowles Machine Learning in Python. Essential Techniques for Predictive Analysis Новинка

Michael Bowles Machine Learning in Python. Essential Techniques for Predictive Analysis

Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions. Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language. Predict outcomes using linear and ensemble algorithm families Build predictive models that solve a range of simple and complex problems Apply core machine learning algorithms using Python Use sample code directly to build custom solutions Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.
Bernhard Pfaff Financial Risk Modelling and Portfolio Optimization with R Bernhard Pfaff Financial Risk Modelling and Portfolio Optimization with R Новинка

Bernhard Pfaff Financial Risk Modelling and Portfolio Optimization with R

Financial Risk Modelling and Portfolio Optimization with R, 2nd Edition Bernhard Pfaff, Invesco Global Asset Allocation, Germany A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Is accompanied by a supporting website featuring examples and case studies in R. Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.
Frank Fabozzi J. Robust Equity Portfolio Management. Formulations, Implementations, and Properties using MATLAB Frank Fabozzi J. Robust Equity Portfolio Management. Formulations, Implementations, and Properties using MATLAB Новинка

Frank Fabozzi J. Robust Equity Portfolio Management. Formulations, Implementations, and Properties using MATLAB

A comprehensive portfolio optimization guide, with provided MATLAB code Robust Equity Portfolio Management + Website offers the most comprehensive coverage available in this burgeoning field. Beginning with the fundamentals before moving into advanced techniques, this book provides useful coverage for both beginners and advanced readers. MATLAB code is provided to allow readers of all levels to begin implementing robust models immediately, with detailed explanations and applications in the equity market included to help you grasp the real-world use of each technique. The discussion includes the most up-to-date thinking and cutting-edge methods, including a much-needed alternative to the traditional Markowitz mean-variance model. Unparalleled in depth and breadth, this book is an invaluable reference for all risk managers, portfolio managers, and analysts. Portfolio construction models originating from the standard Markowitz mean-variance model have a high input sensitivity that threatens optimization, spawning a flurry of research into new analytic techniques. This book covers the latest developments along with the basics, to give you a truly comprehensive understanding backed by a robust, practical skill set. Get up to speed on the latest developments in portfolio optimization Implement robust models using provided MATLAB code Learn advanced optimization methods with equity portfolio applications Understand the formulations, performances, and properties of robust portfolios The Markowitz mean-variance model remains the standard framework for portfolio optimization, but the interest in—and need for—an alternative is rapidly increasing. Resolving the sensitivity issue and dramatically reducing portfolio risk is a major focus of today's portfolio manager. Robust Equity Portfolio Management + Website provides a viable alternative framework, and the hard skills to implement any optimization method.
David Moore Richard Designing Online Learning with Flash David Moore Richard Designing Online Learning with Flash Новинка

David Moore Richard Designing Online Learning with Flash

There is a need for a book that provides a model of learning that is appropriate for online learning as well as teaches the user how to create potent Flash applications to deliver online learning content. This book is an Adobe Flash tutorial set in an instructional design context. It demonstrates how to develop Flash tutorials for teaching facts, concepts, principles, and procedures using Merrill s Component Display Theory. All the book s source files are provided as well as Adobe Captivate tutorials demonstrating the procedures.
Saoussen Krichen Graph-related Optimization and Decision Support Systems Saoussen Krichen Graph-related Optimization and Decision Support Systems Новинка

Saoussen Krichen Graph-related Optimization and Decision Support Systems

Constrained optimization is a challenging branch of operations research that aims to create a model which has a wide range of applications in the supply chain, telecommunications and medical fields. As the problem structure is split into two main components, the objective is to accomplish the feasible set framed by the system constraints. The aim of this book is expose optimization problems that can be expressed as graphs, by detailing, for each studied problem, the set of nodes and the set of edges. This graph modeling is an incentive for designing a platform that integrates all optimization components in order to output the best solution regarding the parameters' tuning. The authors propose in their analysis, for optimization problems, to provide their graphical modeling and mathematical formulation and expose some of their variants. As a solution approaches, an optimizer can be the most promising direction for limited-size instances. For large problem instances, approximate algorithms are the most appropriate way for generating high quality solutions. The authors thus propose, for each studied problem, a greedy algorithm as a problem-specific heuristic and a genetic algorithm as a metaheuristic.
Vangelis Paschos Th. Concepts of Combinatorial Optimization Vangelis Paschos Th. Concepts of Combinatorial Optimization Новинка

Vangelis Paschos Th. Concepts of Combinatorial Optimization

13921.25 руб. Найти похожее
Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aims to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization. Concepts of Combinatorial Optimization, is divided into three parts: On the complexity of combinatorial optimization problems, that presents basics about worst-case and randomized complexity; Classical solution methods, that presents the two most-known methods for solving hard combinatorial optimization problems, that are Branch-and-Bound and Dynamic Programming; Elements from mathematical programming, that presents fundamentals from mathematical programming based methods that are in the heart of Operations Research since the origins of this field.
Evelyne Lutton Evolutionary Algorithms for Food Science and Technology Evelyne Lutton Evolutionary Algorithms for Food Science and Technology Новинка

Evelyne Lutton Evolutionary Algorithms for Food Science and Technology

10357.59 руб. Найти похожее
Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis.
Omid Bozorg-Haddad Meta-heuristic and Evolutionary Algorithms for Engineering Optimization Omid Bozorg-Haddad Meta-heuristic and Evolutionary Algorithms for Engineering Optimization Новинка

Omid Bozorg-Haddad Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

10357.59 руб. Найти похожее
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering; Relates optimization algorithms to engineering problems employing a unifying approach. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science. OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran. MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran. HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.
Sanjay Ranka Modeling and Optimization of Parallel and Distributed Embedded Systems Sanjay Ranka Modeling and Optimization of Parallel and Distributed Embedded Systems Новинка

Sanjay Ranka Modeling and Optimization of Parallel and Distributed Embedded Systems

10740.45 руб. Найти похожее
This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability. Key features: Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems. Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems. Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation. Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies. Highlights research challenges and future research directions. The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research.
Vangelis Paschos Th. Applications of Combinatorial Optimization Vangelis Paschos Th. Applications of Combinatorial Optimization Новинка

Vangelis Paschos Th. Applications of Combinatorial Optimization

Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aim to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization. Concepts of Combinatorial Optimization, is divided into three parts: – On the complexity of combinatorial optimization problems, presenting basics about worst-case and randomized complexity; – Classical solution methods, presenting the two most-known methods for solving hard combinatorial optimization problems, that are Branch-and-Bound and Dynamic Programming; – Elements from mathematical programming, presenting fundamentals from mathematical programming based methods that are in the heart of Operations Research since the origins of this field.
Xin-She Yang Engineering Optimization. An Introduction with Metaheuristic Applications Xin-She Yang Engineering Optimization. An Introduction with Metaheuristic Applications Новинка

Xin-She Yang Engineering Optimization. An Introduction with Metaheuristic Applications

11397.07 руб. Найти похожее
An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms. The author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts: Foundations of Optimization and Algorithms provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo method Metaheuristic Algorithms presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search Applications outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimization Throughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important and popular algorithms using MATLAB® and Octave software packages, and a related FTP site houses MATLAB code and programs for easy implementation of the discussed techniques. In addition, references to the current literature enable readers to investigate individual algorithms and methods in greater detail. Engineering Optimization: An Introduction with Metaheuristic Applications is an excellent book for courses on optimization and computer simulation at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners working in the fields of mathematics, engineering, computer science, operations research, and management science who use metaheuristic algorithms to solve problems in their everyday work.
Bryan Dodson Probabilistic Design for Optimization and Robustness for Engineers Bryan Dodson Probabilistic Design for Optimization and Robustness for Engineers Новинка

Bryan Dodson Probabilistic Design for Optimization and Robustness for Engineers

Probabilistic Design for Optimization and Robustness: Presents the theory of modeling with variation using physical models and methods for practical applications on designs more insensitive to variation. Provides a comprehensive guide to optimization and robustness for probabilistic design. Features examples, case studies and exercises throughout. The methods presented can be applied to a wide range of disciplines such as mechanics, electrics, chemistry, aerospace, industry and engineering. This text is supported by an accompanying website featuring videos, interactive animations to aid the readers understanding.
Vangelis Paschos Th. Paradigms of Combinatorial Optimization. Problems and New Approaches Vangelis Paschos Th. Paradigms of Combinatorial Optimization. Problems and New Approaches Новинка

Vangelis Paschos Th. Paradigms of Combinatorial Optimization. Problems and New Approaches

23249.26 руб. Найти похожее
Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aim to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization. Concepts of Combinatorial Optimization, is divided into three parts: – On the complexity of combinatorial optimization problems, presenting basics about worst-case and randomized complexity; – Classical solution methods, presenting the two most-known methods for solving hard combinatorial optimization problems, that are Branch-and-Bound and Dynamic Programming; – Elements from mathematical programming, presenting fundamentals from mathematical programming based methods that are in the heart of Operations Research since the origins of this field.
Allison Zmuda Learning Personalized. The Evolution of the Contemporary Classroom Allison Zmuda Learning Personalized. The Evolution of the Contemporary Classroom Новинка

Allison Zmuda Learning Personalized. The Evolution of the Contemporary Classroom

A real-world action plan for educators to create personalized learning experiences Learning Personalized: The Evolution of the Contemporary Classroom provides teachers, administrators, and educational leaders with a clear and practical guide to personalized learning. Written by respected teachers and leading educational consultants Allison Zmuda, Greg Curtis, and Diane Ullman, this comprehensive resource explores what personalized learning looks like, how it changes the roles and responsibilities of every stakeholder, and why it inspires innovation. The authors explain that, in order to create highly effective personalized learning experiences, a new instructional design is required that is based loosely on the traditional model of apprenticeship: learning by doing. Learning Personalized challenges educators to rethink the fundamental principles of schooling that honors students' natural willingness to play, problem solve, fail, re-imagine, and share. This groundbreaking resource: Explores the elements of personalized learning and offers a framework to achieve it Provides a roadmap for enrolling relevant stakeholders to create a personalized learning vision and reimagine new roles and responsibilities Addresses needs and provides guidance specific to the job descriptions of various types of educators, administrators, and other staff This invaluable educational resource explores a simple framework for personalized learning: co-creation, feedback, sharing, and learning that is as powerful for a teacher to re-examine classroom practice as it is for a curriculum director to reexamine the structure of courses.
Vangelis Paschos Th. Concepts of Combinatorial Optimization Vangelis Paschos Th. Concepts of Combinatorial Optimization Новинка

Vangelis Paschos Th. Concepts of Combinatorial Optimization

13003.37 руб. Найти похожее
Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aim to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization. Concepts of Combinatorial Optimization, is divided into three parts: – On the complexity of combinatorial optimization problems, presenting basics about worst-case and randomized complexity; – Classical solution methods, presenting the two most-known methods for solving hard combinatorial optimization problems, that are Branch-and-Bound and Dynamic Programming; – Elements from mathematical programming, presenting fundamentals from mathematical programming based methods that are in the heart of Operations Research since the origins of this field.
Vangelis Paschos Th. Applications of Combinatorial Optimization Vangelis Paschos Th. Applications of Combinatorial Optimization Новинка

Vangelis Paschos Th. Applications of Combinatorial Optimization

13921.25 руб. Найти похожее
Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aims to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization. “Applications of Combinatorial Optimization” is presenting a certain number among the most common and well-known applications of Combinatorial Optimization.
Xin-She Yang Optimization Techniques and Applications with Examples Xin-She Yang Optimization Techniques and Applications with Examples Новинка

Xin-She Yang Optimization Techniques and Applications with Examples

A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author—a noted expert in the field—covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics. Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book’s exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource: Offers an accessible and state-of-the-art introduction to the main optimization techniques Contains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques Presents a balance of theory, algorithms, and implementation Includes more than 100 worked examples with step-by-step explanations Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.
SWITCHING POWER SUPPLY DESIGN AND OPTIMIZATION SWITCHING POWER SUPPLY DESIGN AND OPTIMIZATION Новинка

SWITCHING POWER SUPPLY DESIGN AND OPTIMIZATION

SWITCHING POWER SUPPLY DESIGN AND OPTIMIZATION
Guillaume Sandou Metaheuristic Optimization for the Design of Automatic Control Laws Guillaume Sandou Metaheuristic Optimization for the Design of Automatic Control Laws Новинка

Guillaume Sandou Metaheuristic Optimization for the Design of Automatic Control Laws

The classic approach in Automatic Control relies on the use of simplified models of the systems and reformulations of the specifications. In this framework, the control law can be computed using deterministic algorithms. However, this approach fails when the system is too complex for its model to be sufficiently simplified, when the designer has many constraints to take into account, or when the goal is not only to design a control but also to optimize it. This book presents a new trend in Automatic Control with the use of metaheuristic algorithms. These kinds of algorithm can optimize any criterion and constraint, and therefore do not need such simplifications and reformulations. The first chapter outlines the author’s main motivations for the approach which he proposes, and presents the advantages which it offers. In Chapter 2, he deals with the problem of system identification. The third and fourth chapters are the core of the book where the design and optimization of control law, using the metaheuristic method (particle swarm optimization), is given. The proposed approach is presented along with real-life experiments, proving the efficiency of the methodology. Finally, in Chapter 5, the author proposes solving the problem of predictive control of hybrid systems. Contents 1. Introduction and Motivations. 2. Symbolic Regression. 3. PID Design Using Particle Swarm Optimization. 4. Tuning and Optimization of H-infinity Control Laws. 5. Predictive Control of Hybrid Systems. About the Authors Guillaume Sandou is Professor in the Automatic Department of Supélec, in Gif Sur Yvette, France. He has had 12 books, 8 journal papers and 1 patent published, and has written papers for 32 international conferences.His main research interests include modeling, optimization and control of industrial systems; optimization and metaheuristics for Automatic Control; and constrained control.
Harri Holma LTE Small Cell Optimization. 3GPP Evolution to Release 13 Harri Holma LTE Small Cell Optimization. 3GPP Evolution to Release 13 Новинка

Harri Holma LTE Small Cell Optimization. 3GPP Evolution to Release 13

LTE network capabilities are enhanced with small cell deployment, with optimization and with new 3GPP features. LTE networks are getting high loaded which calls for more advanced optimization. Small cells have been discussed in the communications industry for many years, but their true deployment is happening now. New 3GPP features in Release 12 and 13 further push LTE network performance. This timely book addresses R&D and standardization activities on LTE small cells and network optimization, focusing on 3GPP evolution to Release 13. It covers LTE small cells from specification to products and field results; Latest 3GPP evolution to Release 13; and LTE optimization and learnings from the field.
Ad Ridder Fast Sequential Monte Carlo Methods for Counting and Optimization Ad Ridder Fast Sequential Monte Carlo Methods for Counting and Optimization Новинка

Ad Ridder Fast Sequential Monte Carlo Methods for Counting and Optimization

A comprehensive account of the theory and application of Monte Carlo methods Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems. Written by authorities in the field, the book places emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. Focusing on the concepts and application of Monte Carlo techniques, Fast Sequential Monte Carlo Methods for Counting and Optimization includes: Detailed algorithms needed to practice solving real-world problems Numerous examples with Monte Carlo method produced solutions within the 1-2% limit of relative error A new generic sequential importance sampling algorithm alongside extensive numerical results An appendix focused on review material to provide additional background information Fast Sequential Monte Carlo Methods for Counting and Optimization is an excellent resource for engineers, computer scientists, mathematicians, statisticians, and readers interested in efficient simulation techniques. The book is also useful for upper-undergraduate and graduate-level courses on Monte Carlo methods.
Radi Bouchaib Uncertainty and Optimization in Structural Mechanics Radi Bouchaib Uncertainty and Optimization in Structural Mechanics Новинка

Radi Bouchaib Uncertainty and Optimization in Structural Mechanics

Optimization is generally a reduction operation of a definite quantity. This process naturally takes place in our environment and through our activities. For example, many natural systems evolve, in order to minimize their potential energy. Modeling these phenomena then largely relies on our capacity to artificially reproduce these processes. In parallel, optimization problems have quickly emerged from human activities, notably from economic concerns. This book includes the most recent ideas coming from research and industry in the field of optimization, reliability and the recognition of accompanying uncertainties. It is made up of eight chapters which look at the reviewing of uncertainty tools, system reliability, optimal design of structures and their optimization (of sizing, form, topology and multi-objectives) – along with their robustness and issues on optimal safety factors. Optimization reliability coupling will also be tackled in order to take into account the uncertainties in the modeling and resolution of the problems encountered. The book is aimed at students, lecturers, engineers, PhD students and researchers. Contents 1. Uncertainty. 2. Reliability in Mechanical Systems. 3. Optimal Structural Design. 4. Multi-object Optimization with Uncertainty. 5. Robust Optimization. 6. Reliability Optimization. 7. Optimal Security Factors Approach. 8. Reliability-based Topology Optimization. About the Authors Abdelkhalak El Hami is Professor at the Institut National des Sciences Appliquées, Rouen, France. He is the author of many articles and books on optimization and uncertainty. Bouchaib Radi is Professor in the Faculty of Sciences and Technology at the University of Hassan Premier, Settat, Morocco. His research interests are in such areas as structural optimization, parallel computation, contact problem and metal forming. He is the author of many scientific articles and books.
Alice Yalaoui Optimization of Logistics Alice Yalaoui Optimization of Logistics Новинка

Alice Yalaoui Optimization of Logistics

12271.16 руб. Найти похожее
This book aims to help engineers, Masters students and young researchers to understand and gain a general knowledge of logistic systems optimization problems and techniques, such as system design, layout, stock management, quality management, lot-sizing or scheduling. It summarizes the evaluation and optimization methods used to solve the most frequent problems. In particular, the authors also emphasize some recent and interesting scientific developments, as well as presenting some industrial applications and some solved instances from real-life cases. Performance evaluation tools (Petri nets, the Markov process, discrete event simulation, etc.) and optimization techniques (branch-and-bound, dynamic programming, genetic algorithms, ant colony optimization, etc.) are presented first. Then, new optimization methods are presented to solve systems design problems, layout problems and buffer-sizing optimization. Forecasting methods, inventory optimization, packing problems, lot-sizing quality management and scheduling are presented with examples in the final chapters.
Patricia Ruiz Evolutionary Algorithms for Mobile Ad Hoc Networks Patricia Ruiz Evolutionary Algorithms for Mobile Ad Hoc Networks Новинка

Patricia Ruiz Evolutionary Algorithms for Mobile Ad Hoc Networks

Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field. Evolutionary Algorithms for Mobile Ad Hoc Networks: Instructs on how to identify, model, and optimize solutions to problems that arise in daily research Presents complete and up-to-date surveys on topics like network and mobility simulators Provides sample problems along with solutions/descriptions used to solve each, with performance comparisons Covers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and more Evolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.
Huamin Zhou Computer Modeling for Injection Molding. Simulation, Optimization, and Control Huamin Zhou Computer Modeling for Injection Molding. Simulation, Optimization, and Control Новинка

Huamin Zhou Computer Modeling for Injection Molding. Simulation, Optimization, and Control

11507.62 руб. Найти похожее
This book covers a wide range of applications and uses of simulation and modeling techniques in polymer injection molding, filling a noticeable gap in the literature of design, manufacturing, and the use of plastics injection molding. The authors help readers solve problems in the advanced control, simulation, monitoring, and optimization of injection molding processes. The book provides a tool for researchers and engineers to calculate the mold filling, optimization of processing control, and quality estimation before prototype molding.
Dan Simon Evolutionary Computation with Biogeography-based Optimization Dan Simon Evolutionary Computation with Biogeography-based Optimization Новинка

Dan Simon Evolutionary Computation with Biogeography-based Optimization

10357.59 руб. Найти похожее
Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.
Colin McFarlane Learning the City. Knowledge and Translocal Assemblage Colin McFarlane Learning the City. Knowledge and Translocal Assemblage Новинка

Colin McFarlane Learning the City. Knowledge and Translocal Assemblage

Learning the City: Translocal Assemblage and Urban Politics critically examines the relationship between knowledge, learning, and urban politics, arguing both for the centrality of learning for political strategies and developing a progressive international urbanism. Presents a distinct approach to conceptualising the city through the lens of urban learning Integrates fieldwork conducted in Mumbai's informal settlements with debates on urban policy, political economy, and development Considers how knowledge and learning are conceived and created in cities Addresses the way knowledge travels and opportunities for learning about urbanism between North and South
Robert Stewart B. Value Optimization for Project and Performance Management Robert Stewart B. Value Optimization for Project and Performance Management Новинка

Robert Stewart B. Value Optimization for Project and Performance Management

Discover the proven process for maximizing the potential value of any project. Showing readers how to apply value optimization techniques to project and performance management, dramatically increasing results and efficiency, Value Optimization for Project and Performance Management is written to compliment the Project Management Body of Knowledge, the guidance published by the Project Management Institute (PMI®), making it readily applicable for any project manager. Presents methodology applied with hundreds of clients across a range of industries Filled with practical facilitation and implementation tips Presents a cohesive theory, structured framework, and diverse toolset Walks you through the value optimization process, showing you how to transform the way a product or process is perceived Brimming with examples, Value Optimization for Project and Performance Management provides a link to a free software demo for you to get started in applying value optimization in your own organization. (PMI is a registered mark of Project Management Institute, Inc.)
Thierry Benoist Mathematical Programming Solver Based on Local Search Thierry Benoist Mathematical Programming Solver Based on Local Search Новинка

Thierry Benoist Mathematical Programming Solver Based on Local Search

This book covers local search for combinatorial optimization and its extension to mixed-variable optimization. Although not yet understood from the theoretical point of view, local search is the paradigm of choice for tackling large-scale real-life optimization problems. Today’s end-users demand interactivity with decision support systems. For optimization software, this means obtaining good-quality solutions quickly. Fast iterative improvement methods, like local search, are suited to satisfying such needs. Here the authors show local search in a new light, in particular presenting a new kind of mathematical programming solver, namely LocalSolver, based on neighborhood search. First, an iconoclast methodology is presented to design and engineer local search algorithms. The authors’ concern regarding industrializing local search approaches is of particular interest for practitioners. This methodology is applied to solve two industrial problems with high economic stakes. Software based on local search induces extra costs in development and maintenance in comparison with the direct use of mixed-integer linear programming solvers. The authors then move on to present the LocalSolver project whose goal is to offer the power of local search through a model-and-run solver for large-scale 0-1 nonlinear programming. They conclude by presenting their ongoing and future work on LocalSolver toward a full mathematical programming solver based on local search.
Goodith White Technology Enhanced Language Learning: connecting theory and practice Goodith White Technology Enhanced Language Learning: connecting theory and practice Новинка

Goodith White Technology Enhanced Language Learning: connecting theory and practice

How can you use technology for pedagogic purposes in the language classroom? Technology Enhanced Language Learning discusses how the use of technology opens up opportunities for learning, how it enables different types of learning, and how it affects language use.
Christine Solnon Ant Colony Optimization and Constraint Programming Christine Solnon Ant Colony Optimization and Constraint Programming Новинка

Christine Solnon Ant Colony Optimization and Constraint Programming

12234.64 руб. Найти похожее
Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search approaches and metaheuristics, and shows how they can be integrated within constraint programming languages. The second part describes the ant colony optimization metaheuristic and illustrates its capabilities on different constraint satisfaction problems. The third part shows how the ant colony may be integrated within a constraint programming language, thus combining the expressive power of constraint programming languages, to describe problems in a declarative way, and the solving power of ant colony optimization to efficiently solve these problems.
Laura Bierema L. Adult Learning. Linking Theory and Practice Laura Bierema L. Adult Learning. Linking Theory and Practice Новинка

Laura Bierema L. Adult Learning. Linking Theory and Practice

Solidly grounded in theory and research, but concise and practice-oriented, Adult Learning: Linking Theory and Practice is perfect for master’s-level students and practitioners alike. Sharan Merriam and Laura Bierema have infused each chapter with practical applications for instruction which will help readers personally relate to the material. The contents covers: Adult Learning in Today’s World Traditional Learning Theories Andragogy Self-Directed Learning Transformative Learning Experience and Learning Body and Spirit in Learning Motivation and Learning The Brain and Cognitive Functioning Adult Learning in the Digital Age Critical Thinking and Critical Perspectives Culture and Context Discussion questions and activities for reflection are included at the end of each chapter.
Pamela Eddy L. Connecting Learning Across the Institution. New Directions for Higher Education, Number 165 Pamela Eddy L. Connecting Learning Across the Institution. New Directions for Higher Education, Number 165 Новинка

Pamela Eddy L. Connecting Learning Across the Institution. New Directions for Higher Education, Number 165

Most research on learning tends to occur in silos based on stakeholder perspective. This volume seeks to break down these silos and draw together scholars who research learning from different perspectives to highlight commonalities in learning for students, faculty, and institutions. When we understand how learning is experienced across the institution, we can develop strategies that help support, enhance, and reinforce learning for all. Exploring what it means to bridge learning across the institution, this volume provides a roadmap to improve learning for all. Both scholarly and practical, it advances the knowledge about the ways we investigate and study learning across and for various groups of learners. It also: Collects thinking about learning in its various formats in one location Provides a platform for synthesis Outlines key questions for thinking more deeply about learning on campus. Instead of thinking of learning as discrete depending on the stakeholder group, this volume highlights the commonalities across all types of learners.
Jane Bozarth Social Media for Trainers. Techniques for Enhancing and Extending Learning Jane Bozarth Social Media for Trainers. Techniques for Enhancing and Extending Learning Новинка

Jane Bozarth Social Media for Trainers. Techniques for Enhancing and Extending Learning

A how-to resource for incorporating social media into training Whether you work in a traditional or virtual classroom, social media can broaden your reach and increase the impact of training. In Social Media for Trainers, e-learning and new media expert Jane Bozarth provides an overview of popular tools, including blogs, wikis, Twitter, Facebook, YouTube, SlideShare, Flickr, and others. You'll learn to leverage each medium's unique features and applications to deliver training, facilitate discussions, and extend learning beyond the confines of a training event. This key resource offers a new set of powerful tools for augmenting and enhancing the value of your training. PRAISE FOR SOCIAL MEDIA FOR TRAINERS «Clear explanations and practical examples of the use of social media for learning, make this book essential reading for all workplace trainers.» —Jane Hart, founder, Centre for Learning and Performance Technologies, and founding member of the Internet Time Alliance «… a practical, intelligent book teaching trainers how to effectively utilize technology for real learning outcomes.» —Karl Kapp, professor of Instructional Technology at Bloomsburg University and author of Learning in 3D and Gadgets, Games and Gizmos for Learning «Trainers who want to succeed in the new social learning world should read this book. Jane has made social media easy, practical, and simple to use.» —Ray Jimenez, PhD, Chief Learning Architect, VignettesLearning.com
Alan Morris Multidisciplinary Design Optimization Supported by Knowledge Based Engineering Alan Morris Multidisciplinary Design Optimization Supported by Knowledge Based Engineering Новинка

Alan Morris Multidisciplinary Design Optimization Supported by Knowledge Based Engineering

10357.59 руб. Найти похожее
Multidisciplinary Design Optimization supported by Knowledge Based Engineering supports engineers confronting this daunting and new design paradigm. It describes methodology for conducting a system design in a systematic and rigorous manner that supports human creativity to optimize the design objective(s) subject to constraints and uncertainties. The material presented builds on decades of experience in Multidisciplinary Design Optimization (MDO) methods, progress in concurrent computing, and Knowledge Based Engineering (KBE) tools. Key features: Comprehensively covers MDO and is the only book to directly link this with KBE methods Provides a pathway through basic optimization methods to MDO methods Directly links design optimization methods to the massively concurrent computing technology Emphasizes real world engineering design practice in the application of optimization methods Multidisciplinary Design Optimization supported by Knowledge Based Engineering is a one-stop-shop guide to the state-of-the-art tools in the MDO and KBE disciplines for systems design engineers and managers. Graduate or post-graduate students can use it to support their design courses, and researchers or developers of computer-aided design methods will find it useful as a wide-ranging reference.
Ruth Clark C. e-Learning and the Science of Instruction. Proven Guidelines for Consumers and Designers of Multimedia Learning Ruth Clark C. e-Learning and the Science of Instruction. Proven Guidelines for Consumers and Designers of Multimedia Learning Новинка

Ruth Clark C. e-Learning and the Science of Instruction. Proven Guidelines for Consumers and Designers of Multimedia Learning

The essential e-learning design manual, updated with the latest research, design principles, and examples e-Learning and the Science of Instruction is the ultimate handbook for evidence-based e-learning design. Since the first edition of this book, e-learning has grown to account for at least 40% of all training delivery media. However, digital courses often fail to reach their potential for learning effectiveness and efficiency. This guide provides research-based guidelines on how best to present content with text, graphics, and audio as well as the conditions under which those guidelines are most effective. This updated fourth edition describes the guidelines, psychology, and applications for ways to improve learning through personalization techniques, coherence, animations, and a new chapter on evidence-based game design. The chapter on the Cognitive Theory of Multimedia Learning introduces three forms of cognitive load which are revisited throughout each chapter as the psychological basis for chapter principles. A new chapter on engagement in learning lays the groundwork for in-depth reviews of how to leverage worked examples, practice, online collaboration, and learner control to optimize learning. The updated instructor's materials include a syllabus, assignments, storyboard projects, and test items that you can adapt to your own course schedule and students. Co-authored by the most productive instructional research scientist in the world, Dr. Richard E. Mayer, this book distills copious e-learning research into a practical manual for improving learning through optimal design and delivery. Get up to date on the latest e-learning research Adopt best practices for communicating information effectively Use evidence-based techniques to engage your learners Replace popular instructional ideas, such as learning styles with evidence-based guidelines Apply evidence-based design techniques to optimize learning games e-Learning continues to grow as an alternative or adjunct to the classroom, and correspondingly, has become a focus among researchers in learning-related fields. New findings from research laboratories can inform the design and development of e-learning. However, much of this research published in technical journals is inaccessible to those who actually design e-learning material. By collecting the latest evidence into a single volume and translating the theoretical into the practical, e-Learning and the Science of Instruction has become an essential resource for consumers and designers of multimedia learning.
Arce Gonzalo R. Computational Lithography Arce Gonzalo R. Computational Lithography Новинка

Arce Gonzalo R. Computational Lithography

A Unified Summary of the Models and Optimization Methods Used in Computational Lithography Optical lithography is one of the most challenging areas of current integrated circuit manufacturing technology. The semiconductor industry is relying more on resolution enhancement techniques (RETs), since their implementation does not require significant changes in fabrication infrastructure. Computational Lithography is the first book to address the computational optimization of RETs in optical lithography, providing an in-depth discussion of optimal optical proximity correction (OPC), phase shifting mask (PSM), and off-axis illumination (OAI) RET tools that use model-based mathematical optimization approaches. The book starts with an introduction to optical lithography systems, electric magnetic field principles, and the fundamentals of optimization from a mathematical point of view. It goes on to describe in detail different types of optimization algorithms to implement RETs. Most of the algorithms developed are based on the application of the OPC, PSM, and OAI approaches and their combinations. Algorithms for coherent illumination as well as partially coherent illumination systems are described, and numerous simulations are offered to illustrate the effectiveness of the algorithms. In addition, mathematical derivations of all optimization frameworks are presented. The accompanying MATLAB® software files for all the RET methods described in the book make it easy for readers to run and investigate the codes in order to understand and apply the optimization algorithms, as well as to design a set of optimal lithography masks. The codes may also be used by readers for their research and development activities in their academic or industrial organizations. An accompanying MATLAB® software guide is also included. An accompanying MATLAB® software guide is included, and readers can download the software to use with the guide at ftp://ftp.wiley.com/public/sci_tech_med/computational_lithography. Tailored for both entry-level and experienced readers, Computational Lithography is meant for faculty, graduate students, and researchers, as well as scientists and engineers in industrial organizations whose research or career field is semiconductor IC fabrication, optical lithography, and RETs. Computational lithography draws from the rich theory of inverse problems, optics, optimization, and computational imaging; as such, the book is also directed to researchers and practitioners in these fields.
Bernhard Pfaff Financial Risk Modelling and Portfolio Optimization with R Bernhard Pfaff Financial Risk Modelling and Portfolio Optimization with R Новинка

Bernhard Pfaff Financial Risk Modelling and Portfolio Optimization with R

Introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Enables the reader to replicate the results in the book using R code. Is accompanied by a supporting website featuring examples and case studies in R. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.
Elizabeth Barkley F. Learning Assessment Techniques. A Handbook for College Faculty Elizabeth Barkley F. Learning Assessment Techniques. A Handbook for College Faculty Новинка

Elizabeth Barkley F. Learning Assessment Techniques. A Handbook for College Faculty

50 Techniques for Engaging Students and Assessing Learning in College Courses Do you want to: Know what and how well your students are learning? Promote active learning in ways that readily integrate assessment? Gather information that can help make grading more systematic and streamlined? Efficiently collect solid learning outcomes data for institutional assessment? Provide evidence of your teaching effectiveness for promotion and tenure review? Learning Assessment Techniques provides 50 easy-to-implement active learning techniques that gauge student learning across academic disciplines and learning environments. Using Fink's Taxonomy of Significant Learning as its organizational framework, it embeds assessment within active learning activities. Each technique features: purpose and use, key learning goals, step-by-step implementation, online adaptation, analysis and reporting, concrete examples in both on-site and online environments, and key references—all in an easy-to-follow format. The book includes an all-new Learning Goals Inventory, as well as more than 35 customizable assessment rubrics, to help teachers determine significant learning goals and appropriate techniques. Readers will also gain access to downloadable supplements, including a worksheet to guide teachers through the six steps of the Learning Assessment Techniques planning and implementation cycle. College teachers today are under increased pressure to teach effectively and provide evidence of what, and how well, students are learning. An invaluable asset for college teachers of any subject, Learning Assessment Techniques provides a practical framework for seamlessly integrating teaching, learning, and assessment.
Jeanine O'Neill-Blackwell Engage. The Trainer's Guide to Learning Styles Jeanine O'Neill-Blackwell Engage. The Trainer's Guide to Learning Styles Новинка

Jeanine O'Neill-Blackwell Engage. The Trainer's Guide to Learning Styles

Discover Your Training Style Strengths and Build Your Skills with Online Tools, Videos, and More «A superb book that gives learning and development professionals in every industry an automatic must-read. This book is filled with wisdom and insight as well as clear analytic models and real actionable concrete steps.» – Bruce Tulgan, author of It's OK to Be the Boss and Managing Generation X «Engage takes the innovation of 4MAT® and looks at it through the lens of the trainer. An engaging learning experience itself, Engage is full of interactive assessments, links to videos, and foolproof action plans and ideas designed to transform any learning event into a dynamic learning experience.»– Shelley Barnes, executive director, Field Education/Program Development, Aveda Corporation For any trainer who needs easy-to-apply strategies that are grounded in solid research, Engage offers a hands-on guide to understanding learning styles. It includes a four-step model for engaging all learning styles in any learning situation. The book integrates both the art and research-based science of strong instructional design reaching all learning styles with activities, tricks, and tips that are proven to boost skills quickly. Filled with illustrative examples and online companion resources, the book explores the brain research that lays the foundation for the book's 4MAT® model and includes activities and strategies that can be applied for each step in the process. Engage also gives the reader access to an online version of the 4MAT® Training Style Inventory. The results of the assessment give a strengths score in four key training roles.
Brooke Flinders A. Enhancing Teaching and Learning Through Collaborative Structures. New Directions for Teaching and Learning, Number 148 Brooke Flinders A. Enhancing Teaching and Learning Through Collaborative Structures. New Directions for Teaching and Learning, Number 148 Новинка

Brooke Flinders A. Enhancing Teaching and Learning Through Collaborative Structures. New Directions for Teaching and Learning, Number 148

In this volume, the authors contend that teaching and learning must be viewed as communal work, whether conducted in one classroom, with colleagues at a programmatic level, or when tackled on a university-wide scale. When educators partner with faculty colleagues or students in teaching and learning, it becomes possible to improve the educational experiences of all students, model professional behaviors that students will soon be expected to embrace, and positively impact graduates, peers, campuses, and even communities at large. By intentionally creating collaborative structures for communal work to occur, educators can broaden access to opportunities for students, improve engagement experiences within the community, and improve faculty support and scholarship. Exploring multiple perspectives on collaborative structures in teaching and learning, this volume discusses ways to consider the collaborative structures within education that allow for shared contributions to teaching and learning. It discusses the need for practitioners to view teaching and learning as truly communal work, regardless of the type of setting. This is the 148th volume of this Jossey-Bass higher education series. It offers a comprehensive range of ideas and techniques for improving college teaching based on the experience of seasoned instructors and the latest findings of educational and psychological researchers.

кешбака
Страницы:


A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering; Relates optimization algorithms to engineering problems employing a unifying approach. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science. OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran. MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran. HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.
Продажа specialized model learning for optimization лучших цены всего мира
Посредством этого сайта магазина - каталога товаров мы очень легко осуществляем продажу specialized model learning for optimization у одного из интернет-магазинов проверенных фирм. Определитесь с вашими предпочтениями один интернет-магазин, с лучшей ценой продукта. Прочитав рекомендации по продаже specialized model learning for optimization легко охарактеризовать производителя как превосходную и доступную фирму.