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Erschienen in:
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2012 | OriginalPaper | Buchkapitel

Coevolutionary Principles

verfasst von : Elena Popovici, Anthony Bucci, R. Paul Wiegand, Edwin D. De Jong

Erschienen in: Handbook of Natural Computing

Verlag: Springer Berlin Heidelberg

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Abstract

Coevolutionary algorithms approach problems for which no function for evaluating potential solutions is present or known. Instead, algorithms rely on the aggregation of outcomes from interactions among evolving entities in order to make selection decisions. Given the lack of an explicit yardstick, understanding the dynamics of coevolutionary algorithms, judging whether a given algorithm is progressing, and designing effective new algorithms present unique challenges unlike those faced by optimization or evolutionary algorithms. The purpose of this chapter is to provide a foundational understanding of coevolutionary algorithms and to highlight critical theoretical and empirical work done over the last two decades. This chapter outlines the ends and means of coevolutionary algorithms: what they are meant to find, and how they should find it.

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Vorheriges Kapitel Genetics-Based Machine Learning
Nächstes Kapitel Niching in Evolutionary Algorithms
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Metadaten
Titel
Coevolutionary Principles
verfasst von
Elena Popovici
Anthony Bucci
R. Paul Wiegand
Edwin D. De Jong
Copyright-Jahr
2012
Verlag
Springer Berlin Heidelberg
Buch
Handbook of Natural Computing

Print ISBN: 978-3-540-92909-3

Electronic ISBN: 978-3-540-92910-9

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-3-540-92910-9_31

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