Evolutionary Computation for Modeling and Optimization (Hardcover)

Daniel Ashlock

ISBN 10: 0387221964 / ISBN 13: 9780387221960

Verlag: Springer-Verlag New York Inc., New York, NY, 2005

Beschreibung

Hardcover. Evolutionary computation includes Genetic Algorithms, Evolutionary Programming, Evolution Strategies, and Genetic Programming. In general any population based, selectionist algorithm that performs optimization or supports modeling is a form of evolutionary computation. This text covers primarily genetic algorithms and genetic programming as well as variations based on student projects and the author's research. It substantially reflects engineering (problem solving) rather than mathematical (theorem proving) methods. This book should appeal to undergraduates and beginning graduates in mathematics, computer science, engineering and biology. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verkäufers 9780387221960

Bibliografische Details

• Titel: Evolutionary Computation for Modeling and Optimization (Hardcover)
• Verlag: Springer-Verlag New York Inc., New York, NY
• Erscheinungsjahr: 2005
• Sprache: Englisch
• ISBN-10: 0387221964
• ISBN-13: 9780387221960
• Einband: Hardcover
• Zustand: new

Über diesen Titel

Inhaltsangabe

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

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

Von der hinteren Coverseite

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.