research-article

Design of test problems for discrete estimation of distribution algorithms

Authors:

Shih-Ming Wang,

Tian-Li YuAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 407 - 414

https://doi.org/10.1145/2463372.2463419

Published: 06 July 2013 Publication History

Abstract

Two types of problem structures, overlapping and conflict structures, are challenging for the estimation of distribution algorithms (EDAs) to solve. To test the capabilities of different EDAs of dealing with overlapping and conflict structures, some test problems have been proposed. However, the upper-bound of the degree of overlap and the effect of conflict have not been fully investigated. This paper investigates how to properly define the degree of overlap and the degree of conflict to reflect the difficulties of problems for the EDAs. A new test problem is proposed with the new definitions of the degree of overlap and the degree of conflict. A framework for building the proposed problem is presented, and some model-building genetic algorithms are tested by the problem. This test problem can be applied to further researches on overlapping and conflict structures.

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Cited By

Caldwell JKnowles JThies CKubacki FWatson R(2021)Deep Optimisation: Multi-scale Evolution by Inducing and Searching in Deep RepresentationsApplications of Evolutionary Computation10.1007/978-3-030-72699-7_32(506-521)Online publication date: 7-Apr-2021
https://dl.acm.org/doi/10.1007/978-3-030-72699-7_32

Index Terms

Design of test problems for discrete estimation of distribution algorithms
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Caldwell JKnowles JThies CKubacki FWatson R(2021)Deep Optimisation: Multi-scale Evolution by Inducing and Searching in Deep RepresentationsApplications of Evolutionary Computation10.1007/978-3-030-72699-7_32(506-521)Online publication date: 7-Apr-2021
https://dl.acm.org/doi/10.1007/978-3-030-72699-7_32

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