Sumathi - Evolutionary Intelligence - An Introduction to Theory and Applications with Matlab (Springer, 2008).pdf

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Evolutionary Intelligence
S. Sumathi
·
T. Hamsapriya
·
P. Surekha
Evolutionary Intelligence
An Introduction to Theory and Applications
with Matlab
654144912.001.png
S. Sumathi
T. Hamsapriya
P. Surekha
PSG College of Technology
Coimbatore
India
ISBN: 978-3-540-75158-8
e-ISBN: 978-3-540-75382-7
Library of Congress Control Number: 2007938318
2008 Springer-Verlag Berlin Heidelberg
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is
concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting,
reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication
or parts thereof is permitted only under the provisions of the German Copyright Law of September 9,
1965, in its current version, and permission for use must always be obtained from Springer. Violations are
liable to prosecution under the German Copyright Law.
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply,
even in the absence of a specific statement, that such names are exempt from the relevant protective laws
and regulations and therefore free for general use.
Cover Design: Erich Kirchner, Heidelberg
Printed on acid-free paper
987654321
springer.com
c
Preface
EVOLUTIONARY INTELLIGENCE: Theoretical Advances and Applications in
Evolutionary Algorithms, Genetic Algorithms, Genetic Programming and Parallel
Genetic Algorithms is intended to help, provide basic information, and serve as a
first straw for individuals, who are stranded in the mind boggling universe of Evo-
lutionary Computation (EC). Over the course of the past years, global optimization
algorithms imitating certain principles of nature have proved their usefulness in var-
ious domains of applications. Especially worth learning are those principles where
nature has found “stable islands” in a “turbulent ocean” of solution possibilities.
Such phenomena can be found in annealing processes, central nervous systems and
biological Evolution, which in turn have lead to the following optimization meth-
ods: Simulated Annealing (SA), Artificial Neural Networks (ANNs) and the field of
Evolutionary Computation (EC).
EC may currently be characterized by the following pathways: Genetic Algo-
rithms (GA), Evolutionary Programming (EP), Evolution Strategies (ES), Classi-
fier Systems (CFS), Genetic Programming (GP), and several other problem solving
strategies, that are based upon biological observations, that date back to Charles
Darwin’s discoveries in the 19th century: the means of natural selection and the sur-
vival of the fittest, and theories of evolution. The inspired algorithms are thus termed
Evolutionary Algorithms (EA).
Despite decades of work in evolutionary algorithms, there remains a lot of un-
certainty as to when it is beneficial or detrimental to use recombination or mutation.
This book provides a characterization of the roles that recombination and muta-
tion play in evolutionary algorithms. Primary areas of coverage include the theory,
implementation, and application of genetic algorithms (GAs), genetic programming
(GP), parallel genetic algorithm (PGAs) and other variants of genetic and evolution-
ary computation. This book is ideal for researchers, engineers, computer scientists,
graduate students who consider what frontiers await their exploration.
v
vi
Preface
About the Book
This book gives a good introduction to evolutionary computation for those who are
first entering the field and are looking for insight into the underlying mechanisms
behind them. Emphasizing the scientific and machine learning applications of ge-
netic algorithms instead of applications to optimization and engineering, the book
could serve well in an actual course on adaptive algorithms. The author includes
excellent problem sets, these being divided up into “thought exercises” and “com-
puter exercises” in genetic algorithm. Practical use of genetic algorithms demands
an understanding of how to implement them, and the author does so in the last two
chapters of the book by giving the applications in various fields. This book also out-
lines some ideas on when genetic algorithms and genetic programming should be
used, and this is useful since a newcomer to the field may be tempted to view a ge-
netic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of
using a genetic algorithm is how to encode the population, and the author discusses
various ways to do this. Various “exotic” approaches to improve the performance
of genetic algorithms are also discussed such as the “messy” genetic algorithms,
adaptive genetic algorithm and hybrid genetic algorithm.
Salient Features
The salient features of this book includes,
Detailed description on Evolutionary Computation Concepts such as Evolution-
ary Algorithms, Genetic Algorithm and Genetic Programming
A thorough understanding of Parallel Genetic Algorithm and its models
Worked out examples using MATLAB software
Application case studies based on MATLAB in various fields like Control Sys-
tems, Electronics, Image Processing, Power Electronics, Signal Processing, Com-
munication and VLSI Design.
MATLAB Toolboxes for Evolutionary Computation, Research Projects, Genetic
Algorithm Codes in ‘C’ language, Abbreviations EC class/code libraries and
software kits & Glossary.
Organization of the Book
Chapter 1 describes the history of evolutionary computation in brief and discusses
the biological and artificial evolutionary process. The basic principle of evolution –
Darwin’s theory of evolution is described in detail in this chapter. A note on Genetics
and the four important paradigms of EC are discussed in detail. An introduction to

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