Evolutionary Computation for Modeling and Optimization

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Springer Science & Business Media, Apr 4, 2006 - Computers - 572 pages

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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Contents

An Overview of Evolutionary Computation
1
Designing Simple Evolutionary Algorithms 33
32
Optimizing RealValued Functions
67
Coevolving Strings 99
98
Symbots
119
Evolving Finite State Automata
143
PlusOneRecallStore
207
Fitting to Data
231
Problems
291
Application to Bioinformatics 425
424
Glossary
473
A Example Experiment Report
507
B Probability Theory
519
A Review of Calculus and Vectors 537
536
Combinatorial Graphs
545
References
555

Discrete Robotics
263
5
289

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