Evolutionary Computation for Modeling and Optimization - Softcover

Ashlock, Daniel

Softcover

ISBN 10: 1441919694 ISBN 13: 9781441919694

Verlag: Springer, 2010

Inhaltsangabe

Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, selectionist algorithms that operate on populations of structures. It includes over 100 experiments and over 700 homework problems that introduce the topic with an application-oriented approach. Engineering, computer science, and applied math students will find the book a useful guide to using evolutionary algorithms as a problem solving tool. No previous familliarity with evolutionary computation is assumed. The topics 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. The problem oriented approach of the book makes it a good text for engineering of computer science application classes.

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Über die Autorin bzw. den Autor

Dr. Eun-Youn Kim is a mathematician with a background in graph theory and evolutionary game theory, having worked extensively on the issue of representation in the design of gameplaying agents. Dr. Kim received her Ph.D. in mathematics from Iowa State University. She has been employed at the National Institute of Mathematical Sciences and the Korea Institute of Bioscience and Biotechnology in Korea. Dr. Kims research interests include broad interests in evolutionary computation applied to game playing and network modeling in biological systems. She is a member of the IEEE Computational Intelligence Societies technical committee on games and has helped organize international games conferences. Dr. Kim is currently employed by the department of basic science in Hanbat National University in South Korea.Dr. Daniel Ashlock is a professor of mathematics at the University of Guelph in Ontario, Canada. Dr. Ashlock received his Ph.D. in mathematics from Caltech with a focus in algebraic combinatorics. He was employed at Iowa State University before moving to Canada. Dr. Ashlock works on representation issues in evolutionary computation including games, optimization, bioinformatics, and theoretical biology. He holds the Bioinformatics Chair in the Department of Mathematics and Statistics at Guelph and serves on the editorial board of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Games, The IEEE/ACM Transactions on Bioinformatics and Computational Biology, Biosystems, and Game and Puzzle Design. Dr. Ashlock serves on the IEEE Computational Intelligence Societies technical committees on games and bioinformatics and biomedical engineering.

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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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2010
• Sprache: Englisch
• ISBN 10: 1441919694
• ISBN 13: 9781441919694
• Einband: Taschenbuch
• Anzahl der Seiten: 592
• Kontakt zum Hersteller: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Deutschland