Evolutionary Computation for Modeling and Optimization (Interdisciplinary Applied Mathematics)

Daniel Ashlock

ISBN 10: 0387221964 / ISBN 13: 9780387221960

Editore: Springer, 2005

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This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1000grams, ISBN:9780387221960. Codice articolo 7756409

Dati bibliografici

Titolo: Evolutionary Computation for Modeling and ...

Casa editrice: Springer

Data di pubblicazione: 2005

Legatura: Rilegato

Condizione: Good

Descrizione articolo

Riassunto:

Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets.

Lots of applications and test problems, including a biotechnology chapter.

Dalla quarta di copertina:

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.