Evolutionary Computation
Evolutionary Computation

Author: Kenneth A. De Jong

Publisher: MIT Press

Published: 2006-02-03

Total Pages: 267

ISBN-13: 0262303337

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A clear and comprehensive introduction to the field of evolutionary computation that takes an integrated approach. Evolutionary computation, the use of evolutionary systems as computational processes for solving complex problems, is a tool used by computer scientists and engineers who want to harness the power of evolution to build useful new artifacts, by biologists interested in developing and testing better models of natural evolutionary systems, and by artificial life scientists for designing and implementing new artificial evolutionary worlds. In this clear and comprehensive introduction to the field, Kenneth De Jong presents an integrated view of the state of the art in evolutionary computation. Although other books have described such particular areas of the field as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, Evolutionary Computation is noteworthy for considering these systems as specific instances of a more general class of evolutionary algorithms. This useful overview of a fragmented field is suitable for classroom use or as a reference for computer scientists and engineers.

Evolutionary Computation for Modeling and Optimization
Evolutionary Computation for Modeling and Optimization

Author: Daniel Ashlock

Publisher: Springer Science & Business Media

Published: 2006-04-04

Total Pages: 578

ISBN-13: 0387319093

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Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.

Theory of Evolutionary Computation
Theory of Evolutionary Computation

Author: Benjamin Doerr

Publisher: Springer Nature

Published: 2019-11-20

Total Pages: 527

ISBN-13: 3030294145

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This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influences performance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming. Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing.

Artificial Intelligence and Evolutionary Computations in Engineering Systems
Artificial Intelligence and Evolutionary Computations in Engineering Systems

Author: Subhransu Sekhar Dash

Publisher: Springer

Published: 2018-03-19

Total Pages: 714

ISBN-13: 9811078688

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The book is a collection of high-quality peer-reviewed research papers presented in the International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES 2017). The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. Researchers from academia and industry have presented their original work and ideas, information, techniques and applications in the field of communication, computing and power technologies.

Evolutionary Computation 1
Evolutionary Computation 1

Author: Thomas Baeck

Publisher: CRC Press

Published: 2018-10-03

Total Pages: 374

ISBN-13: 1351989421

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The field of evolutionary computation is expanding dramatically, fueled by the vast investment that reflects the value of applying its techniques. Culling material from the Handbook of Evolutionary Computation, Evolutionary Computation 1: Basic Algorithms and Operators contains up-to-date information on algorithms and operators used in evolutionary computing. This volume discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is intended to be used by individual researchers, teachers, and students working and studying in this expanding field.

Evolutionary Computation in Bioinformatics
Evolutionary Computation in Bioinformatics

Author: Gary B. Fogel

Publisher: Elsevier

Published: 2002-09-27

Total Pages: 425

ISBN-13: 0080506089

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Bioinformatics has never been as popular as it is today. The genomics revolution is generating so much data in such rapid succession that it has become difficult for biologists to decipher. In particular, there are many problems in biology that are too large to solve with standard methods. Researchers in evolutionary computation (EC) have turned their attention to these problems. They understand the power of EC to rapidly search very large and complex spaces and return reasonable solutions. While these researchers are increasingly interested in problems from the biological sciences, EC and its problem-solving capabilities are generally not yet understood or applied in the biology community.This book offers a definitive resource to bridge the computer science and biology communities. Gary Fogel and David Corne, well-known representatives of these fields, introduce biology and bioinformatics to computer scientists, and evolutionary computation to biologists and computer scientists unfamiliar with these techniques. The fourteen chapters that follow are written by leading computer scientists and biologists who examine successful applications of evolutionary computation to various problems in the biological sciences.* Describes applications of EC to bioinformatics in a wide variety of areas including DNA sequencing, protein folding, gene and protein classification, drug targeting, drug design, data mining of biological databases, and biodata visualization.* Offers industrial and academic researchers in computer science, biology, and bioinformatics an important resource for applying evolutionary computation.* Includes a detailed appendix of biological data resources.

Evolution as Computation
Evolution as Computation

Author: Laura F. Landweber

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 348

ISBN-13: 364255606X

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The study of the genetic basis for evolution has flourished in this century, as well as our understanding of the evolvability and programmability of biological systems. Genetic algorithms meanwhile grew out of the realization that a computer program could use the biologically-inspired processes of mutation, recombination, and selection to solve hard optimization problems. Genetic and evolutionary programming provide further approaches to a wide variety of computational problems. A synthesis of these experiences reveals fundamental insights into both the computational nature of biological evolution and processes of importance to computer science. Topics include biological models of nucleic acid information processing and genome evolution; molecules, cells, and metabolic circuits that compute logical relationships; the origin and evolution of the genetic code; and the interface with genetic algorithms and genetic and evolutionary programming.

Evolutionary Computations
Evolutionary Computations

Author: Keigo Watanabe

Publisher: Springer

Published: 2012-11-02

Total Pages: 183

ISBN-13: 354039883X

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Evolutionary computation, a broad field that includes genetic algorithms, evolution strategies, and evolutionary programming, has proven to offer well-suited techniques for industrial and management tasks - therefore receiving considerable attention from scientists and engineers during the last decade. This monograph develops and analyzes evolutionary algorithms that can be successfully applied to real-world problems such as robotic control. Although of particular interest to robotic control engineers, Evolutionary Computations also may interest the large audience of researchers, engineers, designers and graduate students confronted with complicated optimization tasks.

Introduction to Evolutionary Computing
Introduction to Evolutionary Computing

Author: A.E. Eiben

Publisher: Springer Science & Business Media

Published: 2007-08-06

Total Pages: 328

ISBN-13: 9783540401841

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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.

Experimental Research in Evolutionary Computation
Experimental Research in Evolutionary Computation

Author: Thomas Bartz-Beielstein

Publisher: Springer Science & Business Media

Published: 2006-05-09

Total Pages: 221

ISBN-13: 354032027X

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This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.