Evolutionary Algorithms

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Introduction to Evolutionary Algorithms

Xinjie Yu,Mitsuo Gen
Introduction to Evolutionary Algorithms Book PDF

Author : Xinjie Yu,Mitsuo Gen
Publisher : Springer Science & Business Media
Page : 427 pages
File Size : 43,5 Mb
Release : 2010-06-10
Category : Computers
ISBN : 9781849961295

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Introduction to Evolutionary Algorithms by Xinjie Yu,Mitsuo Gen Pdf

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.

Evolutionary Algorithms for Solving Multi-Objective Problems

Carlos Coello Coello,Gary B. Lamont,David A. van Veldhuizen
Evolutionary Algorithms for Solving Multi Objective Problems Book PDF

Author : Carlos Coello Coello,Gary B. Lamont,David A. van Veldhuizen
Publisher : Springer Science & Business Media
Page : 810 pages
File Size : 48,9 Mb
Release : 2007-08-26
Category : Computers
ISBN : 9780387367972

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Evolutionary Algorithms for Solving Multi-Objective Problems by Carlos Coello Coello,Gary B. Lamont,David A. van Veldhuizen Pdf

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.

Evolutionary Algorithms

William M. Spears
Evolutionary Algorithms Book PDF

Author : William M. Spears
Publisher : Springer Science & Business Media
Page : 224 pages
File Size : 52,9 Mb
Release : 2013-03-09
Category : Computers
ISBN : 9783662041994

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Evolutionary Algorithms by William M. Spears Pdf

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

Alain Petrowski,Sana Ben-Hamida
Evolutionary Algorithms Book PDF

Author : Alain Petrowski,Sana Ben-Hamida
Publisher : John Wiley & Sons
Page : 256 pages
File Size : 46,5 Mb
Release : 2017-04-24
Category : Computers
ISBN : 9781848218048

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Evolutionary Algorithms by Alain Petrowski,Sana Ben-Hamida Pdf

Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.

Evolutionary Optimization Algorithms

Dan Simon
Evolutionary Optimization Algorithms Book PDF

Author : Dan Simon
Publisher : John Wiley & Sons
Page : 776 pages
File Size : 41,8 Mb
Release : 2013-06-13
Category : Mathematics
ISBN : 9781118659502

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Evolutionary Optimization Algorithms by Dan Simon Pdf

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 and Neural Networks

Seyedali Mirjalili
Evolutionary Algorithms and Neural Networks Book PDF

Author : Seyedali Mirjalili
Publisher : Springer
Page : 156 pages
File Size : 43,6 Mb
Release : 2018-06-26
Category : Technology & Engineering
ISBN : 9783319930251

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Evolutionary Algorithms and Neural Networks by Seyedali Mirjalili Pdf

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.

Parameter Setting in Evolutionary Algorithms

F.J. Lobo,Cláudio F. Lima,Zbigniew Michalewicz
Parameter Setting in Evolutionary Algorithms Book PDF

Author : F.J. Lobo,Cláudio F. Lima,Zbigniew Michalewicz
Publisher : Springer
Page : 318 pages
File Size : 44,5 Mb
Release : 2007-04-03
Category : Technology & Engineering
ISBN : 9783540694328

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Parameter Setting in Evolutionary Algorithms by F.J. Lobo,Cláudio F. Lima,Zbigniew Michalewicz Pdf

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.

Introduction to Evolutionary Computing

Agoston E. Eiben,J.E. Smith
Introduction to Evolutionary Computing Book PDF

Author : Agoston E. Eiben,J.E. Smith
Publisher : Springer Science & Business Media
Page : 307 pages
File Size : 45,6 Mb
Release : 2013-03-14
Category : Computers
ISBN : 9783662050941

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Introduction to Evolutionary Computing by Agoston E. Eiben,J.E. Smith Pdf

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.

Evolutionary Computation

Kenneth A. De Jong
Evolutionary Computation Book PDF

Author : Kenneth A. De Jong
Publisher : MIT Press
Page : 267 pages
File Size : 41,6 Mb
Release : 2006-02-03
Category : Computers
ISBN : 9780262041942

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Evolutionary Computation by Kenneth A. De Jong Pdf

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.

An Introduction to Genetic Algorithms

Melanie Mitchell
An Introduction to Genetic Algorithms Book PDF

Author : Melanie Mitchell
Publisher : MIT Press
Page : 226 pages
File Size : 46,6 Mb
Release : 1998-03-02
Category : Computers
ISBN : 0262631857

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An Introduction to Genetic Algorithms by Melanie Mitchell Pdf

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.

Evolutionary Algorithms and Chaotic Systems

Ivan Zelinka,Sergej Celikovský,Hendrik Richter,Guanrong Chen
Evolutionary Algorithms and Chaotic Systems Book PDF

Author : Ivan Zelinka,Sergej Celikovský,Hendrik Richter,Guanrong Chen
Publisher : Springer
Page : 560 pages
File Size : 48,5 Mb
Release : 2010-03-10
Category : Technology & Engineering
ISBN : 9783642107078

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Evolutionary Algorithms and Chaotic Systems by Ivan Zelinka,Sergej Celikovský,Hendrik Richter,Guanrong Chen Pdf

This book discusses the mutual intersection of two fields of research: evolutionary computation, which can handle tasks such as control of various chaotic systems, and deterministic chaos, which is investigated as a behavioral part of evolutionary algorithms.

Evolutionary Algorithms in Management Applications

Jörg Biethahn,Volker Nissen
Evolutionary Algorithms in Management Applications Book PDF

Author : Jörg Biethahn,Volker Nissen
Publisher : Springer Science & Business Media
Page : 384 pages
File Size : 51,6 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9783642612176

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Evolutionary Algorithms in Management Applications by Jörg Biethahn,Volker Nissen Pdf

Evolutionary Algorithms (EA) are powerful search and optimisation techniques inspired by the mechanisms of natural evolution. They imitate, on an abstract level, biological principles such as a population based approach, the inheritance of information, the variation of information via crossover/mutation, and the selection of individuals based on fitness. The most well-known class of EA are Genetic Algorithms (GA), which have received much attention not only in the scientific community lately. Other variants of EA, in particular Genetic Programming, Evolution Strategies, and Evolutionary Programming are less popular, though very powerful too. Traditionally, most practical applications of EA have appeared in the technical sector. Management problems, for a long time, have been a rather neglected field of EA-research. This is surprising, since the great potential of evolutionary approaches for the business and economics domain was recognised in pioneering publications quite a while ago. John Holland, for instance, in his seminal book Adaptation in Natural and Artificial Systems (The University of Michigan Press, 1975) identified economics as one of the prime targets for a theory of adaptation, as formalised in his reproductive plans (later called Genetic Algorithms).

Variants of Evolutionary Algorithms for Real-World Applications

Raymond Chiong,Thomas Weise,Zbigniew Michalewicz
Variants of Evolutionary Algorithms for Real World Applications Book PDF

Author : Raymond Chiong,Thomas Weise,Zbigniew Michalewicz
Publisher : Springer Science & Business Media
Page : 470 pages
File Size : 54,7 Mb
Release : 2011-11-13
Category : Technology & Engineering
ISBN : 9783642234248

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Variants of Evolutionary Algorithms for Real-World Applications by Raymond Chiong,Thomas Weise,Zbigniew Michalewicz Pdf

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book “Variants of Evolutionary Algorithms for Real-World Applications” aims to promote the practitioner’s view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the prediction of soil properties, automated tissue classification for MRI images, and database query optimisation, among others. These chapters demonstrate how different types of problems can be successfully solved using variants of EAs and how the solution approaches are constructed, in a way that can be understood and reproduced with little prior knowledge on optimisation.

Evolutionary Algorithms in Engineering Applications

Dipankar Dasgupta,Zbigniew Michalewicz
Evolutionary Algorithms in Engineering Applications Book PDF

Author : Dipankar Dasgupta,Zbigniew Michalewicz
Publisher : Springer Science & Business Media
Page : 561 pages
File Size : 46,8 Mb
Release : 2013-06-29
Category : Computers
ISBN : 9783662034231

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Evolutionary Algorithms in Engineering Applications by Dipankar Dasgupta,Zbigniew Michalewicz Pdf

Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.

Estimation of Distribution Algorithms

Pedro Larrañaga,José A. Lozano
Estimation of Distribution Algorithms Book PDF

Author : Pedro Larrañaga,José A. Lozano
Publisher : Springer Science & Business Media
Page : 382 pages
File Size : 44,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461515395

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Estimation of Distribution Algorithms by Pedro Larrañaga,José A. Lozano Pdf

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. `... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana.