Introduction To Evolutionary Algorithms
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Introduction To Evolutionary Computing
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Author : A.E. Eiben
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-06
Introduction To Evolutionary Computing written by A.E. Eiben and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-06 with Computers categories.
The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.
Introduction To Evolutionary Algorithms
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Author : Xinjie Yu
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-10
Introduction To Evolutionary Algorithms written by Xinjie Yu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-10 with Computers categories.
Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.
An Introduction To Genetic Algorithms
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Author : Melanie Mitchell
language : en
Publisher: MIT Press
Release Date : 1998-03-02
An Introduction To Genetic Algorithms written by Melanie Mitchell and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-03-02 with Computers categories.
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
Introduction To Genetic Algorithms
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Author : S.N. Sivanandam
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-24
Introduction To Genetic Algorithms written by S.N. Sivanandam and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-24 with Technology & Engineering categories.
Theoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses taking place in natural evolution. Although the details of biological evolution are not completely understood (even nowadays), there exist some points supported by strong experimental evidence: • Evolution is a process operating over chromosomes rather than over organisms. The former are organic tools encoding the structure of a living being, i.e., a cr- ture is “built” decoding a set of chromosomes. • Natural selection is the mechanism that relates chromosomes with the ef ciency of the entity they represent, thus allowing that ef cient organism which is we- adapted to the environment to reproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage. There exists a large number of reproductive mechanisms in Nature. Most common ones are mutation (that causes the chromosomes of offspring to be different to those of the parents) and recombination (that combines the chromosomes of the parents to produce the offspring). Based upon the features above, the three mentioned models of evolutionary c- puting were independently (and almost simultaneously) developed.
Evolutionary Optimization Algorithms
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Author : Dan Simon
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-13
Evolutionary Optimization Algorithms written by Dan Simon and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-13 with Mathematics categories.
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.
Evolutionary Algorithms
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Author : William M. Spears
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-06-15
Evolutionary Algorithms written by William M. Spears and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-06-15 with Computers categories.
Despite decades of work in evolutionary algorithms, there remains an uncertainty as to the relative benefits and detriments of using recombination or mutation. This book provides a characterization of the roles that recombination and mutation play in evolutionary algorithms. It integrates important prior work and introduces new theoretical techniques for studying evolutionary algorithms. Consequences of the theory are explored and a novel method for comparing search and optimization algorithms is introduced. The focus allows the book to bridge multiple communities, including evolutionary biologists and population geneticists.
Evolutionary Algorithms In Engineering Applications
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Author : Dipankar Dasgupta
language : en
Publisher: Springer Science & Business Media
Release Date : 1997-05-20
Evolutionary Algorithms In Engineering Applications written by Dipankar Dasgupta and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-05-20 with Computers categories.
Evolutionary algorithms - an overview. Robust encodings in genetic algorithms. Genetic engineering and design problems. The generation of form using an evolutionary approach. Evolutionary optimization of composite structures. Flaw detection and configuration with genetic algorithms. A genetic algorithm approach for river management. Hazards in genetic design methodologies. The identification and characterization of workload classes. Lossless and Lossy data compression. Database design with genetic algorithms. Designing multiprocessor scheduling algorithms using a distributed genetic algorithm system. Prototype based supervised concept learning using genetic algorithms. Prototyping intelligent vehicle modules using evolutionary algorithms. Gate-level evolvable hardware: empirical study and application. Physical design of VLSI circuits and the application of genetic algorithms. Statistical generalization of performance-related heuristcs for knowledge-lean applications. Optimal scheduling of thermal power generation using evolutionary algorithms. Genetic algorithms and genetic programming for control. Global structure evolution and local parameter learning for control system model reductions. Adaptive recursive filtering using evolutionary algorithms. Numerical techniques for efficient sonar bearing and range searching in the near field using genetic algorithms. Signal design for radar imaging in radar astronomy: genetic optimization. Evolutionary algorithms in target acquisition and sensor fusion. Strategies for the integration of evolutionary/ adaptive search with the engineering design process. identification of mechanical inclusions. GeneAS: a robust optimal design technique for mechanical component design. Genetic algorithms for optimal cutting. Practical issues and recent advances in Job- and Open-Shop scheduling. The key steps to achieve mass customization.
Introduction To Fuzzy Logic Using Matlab
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Author : S.N. Sivanandam
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-10-28
Introduction To Fuzzy Logic Using Matlab written by S.N. Sivanandam and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-10-28 with Technology & Engineering categories.
Fuzzy Logic, at present is a hot topic, among academicians as well various programmers. This book is provided to give a broad, in-depth overview of the field of Fuzzy Logic. The basic principles of Fuzzy Logic are discussed in detail with various solved examples. The different approaches and solutions to the problems given in the book are well balanced and pertinent to the Fuzzy Logic research projects. The applications of Fuzzy Logic are also dealt to make the readers understand the concept of Fuzzy Logic. The solutions to the problems are programmed using MATLAB 6.0 and the simulated results are given. The MATLAB Fuzzy Logic toolbox is provided for easy reference.
Evolutionary Computation For Modeling And Optimization
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Author : Daniel Ashlock
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-04
Evolutionary Computation For Modeling And Optimization written by Daniel Ashlock and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-04 with Computers categories.
Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modeling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered. This book presents a large number of homework problems, projects, and experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool.
Evolutionary Computation
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Author : Kenneth A. De Jong
language : en
Publisher: MIT Press
Release Date : 2006-02-03
Evolutionary Computation written by Kenneth A. De Jong and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-02-03 with Computers categories.
This text is an introduction to the field of evolutionary computation. It approaches evolution strategies and genetic programming, as instances of a more general class of evolutionary algorithms.