Working principles, behavior, and performance of MOEAs on MNK-landscapes

This work studies the working principles, behavior, and performance of multiobjective evolutionary algorithms (MOEAs) on multiobjective epistatic fitness functions with discrete binary search spaces by using MNK-landscapes. First, we analyze the structure and some of the properties of MNK-landscapes...

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Published in:	European journal of operational research Vol. 181; no. 3; pp. 1670 - 1690
Main Authors:	Aguirre, Hernán E., Tanaka, Kiyoshi
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 16.09.2007 Elsevier Elsevier Sequoia S.A
Series:	European Journal of Operational Research
Subjects:	Algorithms Discrete binary search spaces Drift Epistasis Evolution & development Evolutionary computations MNK-landscapes Multiobjective combinatorial optimization Multiobjective evolutionary algorithms Mutation Non-linear multiobjective fitness functions Recombination Selection Studies Non-linear multiobjective fitness functions Evolutionary computations Multiobjective evolutionary algorithms Multiobjective combinatorial optimization Recombination MNK-landscapes Selection Epistasis Drift Discrete binary search spaces Mutation
ISSN:	0377-2217, 1872-6860
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Abstract	This work studies the working principles, behavior, and performance of multiobjective evolutionary algorithms (MOEAs) on multiobjective epistatic fitness functions with discrete binary search spaces by using MNK-landscapes. First, we analyze the structure and some of the properties of MNK-landscapes under a multiobjective perspective by using enumeration on small landscapes. Then, we focus on the performance and behavior of MOEAs on large landscapes. We organize our study around selection, drift, mutation, and recombination, the four major and intertwined processes that drive adaptive evolution over fitness landscapes. This work clearly shows pros and cons of the main features of MOEAs, gives a valuable guide for the practitioner on how to set up his/her algorithm, enhance MOEAs, and presents useful insights on how to design more robust and efficient MOEAs.
AbstractList	This work studies the working principles, behavior, and performance of multiobjective evolutionary algorithms (MOEAs) on multiobjective epistatic fitness functions with discrete binary search spaces by using MNK-landscapes. First, we analyze the structure and some of the properties of MNK-landscapes under a multiobjective perspective by using enumeration on small landscapes. Then, we focus on the performance and behavior of MOEAs on large landscapes. We organize our study around selection, drift, mutation, and recombination, the four major and intertwined processes that drive adaptive evolution over fitness landscapes. This work clearly shows pros and cons of the main features of MOEAs, gives a valuable guide for the practitioner on how to set up his/her algorithm, enhance MOEAs, and presents useful insights on how to design more robust and efficient MOEAs. This work studies the working principles, behavior, and performance of multiobjective evolutionary algorithms (MOEAs) on multiobjective epistatic fitness functions with discrete binary search spaces by using MNK-landscapes. First, we analyze the structure and some of the properties of MNK-landscapes under a multiobjective perspective by using enumeration on small landscapes. Then, we focus on the performance and behavior of MOEAs on large landscapes. We organize our study around selection, drift, mutation, and recombination, the four major and intertwined processes that drive adaptive evolution over fitness landscapes. This work clearly shows pros and cons of the main features of MOEAs, gives a valuable guide for the practitioner on how to set up his/her algorithm, enhance MOEAs, and presents useful insights on how to design more robust and efficient MOEAs. [PUBLICATION ABSTRACT]
Author	Aguirre, Hernán E. Tanaka, Kiyoshi
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Keywords	Non-linear multiobjective fitness functions Evolutionary computations Multiobjective evolutionary algorithms Multiobjective combinatorial optimization Recombination MNK-landscapes Selection Epistasis Drift Discrete binary search spaces Mutation
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SubjectTerms	Algorithms Discrete binary search spaces Drift Epistasis Evolution & development Evolutionary computations MNK-landscapes Multiobjective combinatorial optimization Multiobjective evolutionary algorithms Mutation Non-linear multiobjective fitness functions Recombination Selection Studies
Title	Working principles, behavior, and performance of MOEAs on MNK-landscapes
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