Evolutionary Computation for Modeling and Optimization

Name: Evolutionary Computation for Modeling and Optimization
Brand: Springer, Berlin
SKU: 9780387221960
Price: 240.13 PLN
Availability: InStock

Autor: Daniel Ashlock

Język:

Oprawa: Twarda

Wydawca: Springer, Berlin

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Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computatio...

Informacje o książce

Autor

Daniel Ashlock

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2006

strony

550

EAN

9780387221960

ISBN

0387221964

Enbook ID

01380969

Wydawca

Springer, Berlin

Waga

940

Wymiary

156 x 234 x 33

Pełny opis

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.

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