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2013 | OriginalPaper | Chapter

11. Introduction to Evolutionary Algorithms

Authors : Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held

Published in: Computational Intelligence

Publisher: Springer London

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Abstract

Evolutionary algorithms comprise a class of optimization techniques that imitate principles of biological evolution. They belong to the family of metaheuristics, which also includes, for example, particle swarm and ant colony optimization, which are inspired by other biological structures and processes, as well as classical methods like simulated annealing, which is inspired by a thermodynamical process. The core principle of evolutionary algorithms is to apply evolution principles like mutation and selection to populations of candidate solutions in order to find a (sufficiently good) solution for a given optimization problem.

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previous chapter Mathematical Remarks
next chapter Elements of Evolutionary Algorithms
Footnotes
1
The full title “The Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life” is usually shortened to merely “The Origin of Species.”
 
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Metadata
Title
Introduction to Evolutionary Algorithms
Authors
Rudolf Kruse
Christian Borgelt
Frank Klawonn
Christian Moewes
Matthias Steinbrecher
Pascal Held
Copyright Year
2013
Publisher
Springer London
Book
Computational Intelligence

Print ISBN: 978-1-4471-5012-1

Electronic ISBN: 978-1-4471-5013-8

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4471-5013-8_11

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