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This title in other editions

Evolutionary Computation for Modeling and Optimization

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Evolutionary Computation for Modeling and Optimization Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.

Table of Contents

An Overview of Evolutionary Computation.- Designing Simple Evolutionary Algorithms.- Optimizing Real Values Functions.- Sunburn: Co-evolving Strings.- Small Neural Nets: Symbots.- Evolving Finite State Automata.- Ordered Structures.- Plus One Recall Store.- Fitting to Data.- Tartarus: Discrete Robotics.- Evolving Logic Gates.- ISAc lists: Alternative Genetic Programming.- Graph Based Evolutionary Algorithms.- Cellular Encoding.- Application to Bioinformatics.- Glossary.- Appendices.- References.- Index.

Product Details

ISBN:
9781441919694
Author:
Ashlock, Daniel
Publisher:
Springer
Subject:
Applied
Subject:
Algorithms
Subject:
APPLICATIONS OF MATHEMATICS
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Bioinformatics <P>Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, selectionist algorithms that operate on populations of structures. It includes over 100 experiments and over 700 homework problems tha
Subject:
Mathematics-Applied
Subject:
Bioinformatics
Copyright:
Edition Description:
Softcover reprint of hardcover 1st ed. 2006
Publication Date:
20101015
Binding:
TRADE PAPER
Language:
English
Pages:
592
Dimensions:
235 x 155 mm 884 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » Bioinformatics
Computers and Internet » Computers Reference » General
Computers and Internet » Software Engineering » Algorithms
Computers and Internet » Software Engineering » Programming and Languages
Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » Calculus » General
Science and Mathematics » Mathematics » General

Evolutionary Computation for Modeling and Optimization New Trade Paper
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Product details 592 pages Not Avail - English 9781441919694 Reviews:
"Synopsis" by , Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.
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