Sivanandam - Introduction to Genetic Algorithms (Springer, 2008).pdf

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Introduction to Genetic Algorithms
S.N.Sivanandam · S.N.Deepa
Introduction to Genetic
Algorithms
With 193 Figures and 13 Tables
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Authors
S.N.Sivanandam
Professor and Head
Dept. of Computer Science and Engineering
PSG College of Technology
Coimbatore - 641 004
TN, India
S.N.Deepa
Ph.D Scholar Dept. of Computer Science
and Engineering
PSG College of Technology
Coimbatore - 641 004
TN, India
Library of Congress Control Number: 2007930221
ISBN 978-3-540-73189-4 Springer Berlin Heidelberg New York
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 for prosecution under the German Copyright Law.
Springer is a part of Springer Science+Business Media
springer.com
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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
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Cover design: Erich Kirchner, Heidelberg
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Springer-Verlag Berlin Heidelberg 2008
Preface
The origin of evolutionary algorithms was an attempt to mimic some of the processes
taking place in natural evolution. Although the details of biological evolution are
not completely understood (even nowadays), there exist some points supported by
strong experimental evidence:
•
Evolution is a process operating over chromosomes rather than over organisms.
The former are organic tools encoding the structure of a living being, i.e., a crea-
ture is “built” decoding a set of chromosomes.
•
Natural selection is the mechanism that relates chromosomes with the efficiency
of the entity they represent, thus allowing that efficient organism which is well-
adapted to the environment to reproduce more often than those which are not.
•
The evolutionary process takes place during the reproduction stage. There exists
a large number of reproductive mechanisms in Nature. Most common ones are
mutation (that causes the chromosomes of offspring to be different to those of
the parents) and recombination (that combines the chromosomes of the parents
to produce the offspring).
Based upon the features above, the three mentioned models of evolutionary com-
puting were independently (and almost simultaneously) developed.
An Evolutionary Algorithm (EA) is an iterative and stochastic process that op-
erates on a set of individuals (population). Each individual represents a potential
solution to the problem being solved. This solution is obtained by means of a en-
coding/decoding mechanism. Initially, the population is randomly generated (per-
haps with the help of a construction heuristic). Every individual in the population
is assigned, by means of a fitness function, a measure of its goodness with respect
to the problem under consideration. This value is the quantitative information the
algorithm uses to guide the search.
Among the evolutionary techniques, the genetic algorithms (GAs) are the most
extended group of methods representing the application of evolutionary tools. They
rely on the use of a selection, crossover and mutation operators. Replacement is
usually by generations of new individuals.
Intuitively a GA proceeds by creating successive generations of better and better
individuals by applying very simple operations. The search is only guided by the
fitness value associated to every individual in the population. This value is used
to rank individuals depending on their relative suitability for the problem being
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