Daniel Ashlock

Evolutionary Computation for Modeling and Optimization (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 572
• Erscheinungstermin: 4. April 2006
• Englisch
• ISBN-13: 9780387319094
• Artikelnr.: 37285964

Autorenporträt

Daniel Ashlock, University of Guelph, ON, Canada

Inhaltsangabe

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.

Rezensionen

From the reviews: "Evolutionary computation is a rich and diverse field ... . This book ... delivers a very practical introduction to the basics of the field ... . The tasks considered are all very motivational and advance from instructional toy examples to real world applications. ... The particular strength of the book lies in its didactic capabilities. The instructor will find different suggestions for selecting chapters leading to courses with different focus. ... This makes designing courses with the help of this book ... an easy task." (Thomas Jansen, Mathematical Reviews, Issue 2006 k) "This book is based on the author's lecture notes of this lectures given at Iowa State University and is an introduction to evolurionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is intended for computer science, engineering, and other applied mathematics students. ... Finally, the book is a useful guide to using evolutionary algorithms as a problem solving tool." (Emil Ivanov, Zentralblatt MATH, Vol. 1102 (4), 2007) "The present book is mainly focused on genetic algorithms and genetic programming, and successfully explains evolutionary computation through many different applications of these algorithms. ... I enjoyed reading this book ... . All of the chapters of the book are very well written, easy to understand ... . The book could provide a useful background to both undergraduate and graduate students commencing research studies in evolutionary computation. ... very useful for researchers who are planning to develop and apply evolutionary algorithms for their specific problems." (Adil Baykasoglu, The Computer Journal, Vol. 51 (6), 2008)