Evolutionary Computation for Modeling and Optimization

2006. Auflage 2005
ISBN: 978-0-387-22196-0
Verlag: Springer

Buch, Englisch, 572 Seiten, Format (B × H): 156 mm x 243 mm, Gewicht: 2220 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Ashlock, Daniel

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Biowissenschaften Evolutionsbiologie
• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker

Inhaltsverzeichnis

An Overview of Evolutionary Computation.- Designing Simple Evolutionary Algorithms.- Optimizing Real-Valued Functions.- Sunburn: Coevolving Strings.- Small Neural Nets: Symbots.- Evolving Finite State Automata.- Ordered Structures.- Plus-One-Recall-Store.- Fitting to Data.- Tartarus: Discrete Robotics.- Evolving Logic Functions.- ISAc List: Alternative Genetic Programming.- Graph-Based Evolutionary Algorithms.- Cellular Encoding.- Application to Bioinformatics.