research-article

Advanced neighborhoods and problem difficulty measures

Authors:

Mark Hauschild,

Martin PelikanAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 625 - 632

https://doi.org/10.1145/2001576.2001662

Published: 12 July 2011 Publication History

Abstract

While different measures of problem difficulty of fitness landscapes have been proposed, recent studies have shown that many of the common ones do not closely correspond to the actual difficulty of problems when solved by evolutionary algorithms. One of the reasons for this is that most problem difficulty measures are based on neighborhood structures that are quite different from those used in most evolutionary algorithms. This paper examines several ways to increase the accuracy of problem difficulty measures by including linkage information in the measure to more accurately take into account the advanced neighborhoods explored by some evolutionary algorithms. The effects of these modifications of problem difficulty are examined in the context of several simple and advanced evolutionary algorithms. The results are then discussed and promising areas for future research are proposed.

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Cited By

Nikanjam AKarshenas H(2016)Multi-structure problems: Difficult model learning in discrete EDAs2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744226(3448-3454)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7744226
McClymont KWalker DDupenois MMoore J(2012)The lay of the landProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330849(425-432)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330784.2330849
Moser IGheorghita M(2012)Combining search space diagnostics and optimisation2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256454(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6256454
Show More Cited By

Index Terms

Advanced neighborhoods and problem difficulty measures
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
  2. Machine learning
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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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: 12 July 2011

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GECCO '11

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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

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Cited By

Nikanjam AKarshenas H(2016)Multi-structure problems: Difficult model learning in discrete EDAs2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744226(3448-3454)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7744226
McClymont KWalker DDupenois MMoore J(2012)The lay of the landProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330849(425-432)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330784.2330849
Moser IGheorghita M(2012)Combining search space diagnostics and optimisation2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256454(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6256454
Thierens DBosman P(2012)Evolvability analysis of the linkage tree genetic algorithmProceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I10.1007/978-3-642-32937-1_29(286-295)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1007/978-3-642-32937-1_29

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