Evolutionary Computation for Modeling and Optimization (Interdisciplinary Applied Mathematics)

Features

• Cover Type: Hard Cover with 572 pages
• Published by: Springer
• Edition: 1st Edition December 15, 2005
• Written in: English
• ISBN 10 Number: 0387221964
• ISBN 13 Number: 978-0387221960
• Book Dimensions: 9.3 x 6.4 x 1.3 inches
• Weighs: 2.1 pounds

Product Review


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)

Product Description


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.