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Population Size vs. Mutation Strength for the (1+λ) EA on OneMax

Authors:

Christian Gießen,

Carsten WittAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1439 - 1446

https://doi.org/10.1145/2739480.2754738

Published: 11 July 2015 Publication History

Abstract

The (1+1) EA with mutation probability c/n, where c>0 is an arbitrary constant, is studied for the classical OneMax function. Its expected optimization time is analyzed exactly (up to lower order terms) as a function of c and λ. It turns out that 1/n is the only optimal mutation probability if λ=o(ln n ln ln n/ln ln ln n), which is the cut-off point for linear mnspeed-up. However, if λ is above this cut-off point then the standard mutation probability 1/n is no longer the only optimal choice. Instead, the expected number of generations is (up to lower order terms) independent of c, irrespectively of it being less than 1 or greater.

The results are obtained by a careful study of order statistics of the binomial distribution and variable drift theorems for upper and lower bounds.

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Cited By

Doerr BTakadama K(2018)Theory for non-theoreticiansProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3207889(389-414)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3207889
Friedrich TQuinzan FWagner MTakadama K(2018)Escaping large deceptive basins of attraction with heavy-tailed mutation operatorsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205515(293-300)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205515
Gieβen CWitt C(2018)Optimal Mutation Rates for the (1+$$\lambda $$ź) EA on OneMax Through Asymptotically Tight Drift AnalysisAlgorithmica10.1007/s00453-017-0360-y80:5(1710-1731)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s00453-017-0360-y
Show More Cited By

Index Terms

Population Size vs. Mutation Strength for the (1+λ) EA on OneMax
1. Theory of computation
  1. Design and analysis of algorithms

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Published In

cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 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 the author(s) 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: 11 July 2015

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Natur og Univers, Det Frie Forskningsråd

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

Acceptance Rates

GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

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

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Cited By

Doerr BTakadama K(2018)Theory for non-theoreticiansProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3207889(389-414)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3207889
Friedrich TQuinzan FWagner MTakadama K(2018)Escaping large deceptive basins of attraction with heavy-tailed mutation operatorsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205515(293-300)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205515
Gieβen CWitt C(2018)Optimal Mutation Rates for the (1+$$\lambda $$ź) EA on OneMax Through Asymptotically Tight Drift AnalysisAlgorithmica10.1007/s00453-017-0360-y80:5(1710-1731)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s00453-017-0360-y
Doerr BDoerr C(2018)Optimal Static and Self-Adjusting Parameter Choices for the $$(1+(\lambda ,\lambda ))$$(1+(ź,ź)) Genetic AlgorithmAlgorithmica10.1007/s00453-017-0354-980:5(1658-1709)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s00453-017-0354-9
Doerr BLe HMakhmara RNguyen TBosman P(2017)Fast genetic algorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071301(777-784)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071301
Doerr B(2017)Theory for non-theoreticiansProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3067713(389-412)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3067713
Doerr C(2017)Non-static parameter choices in evolutionary computationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3067707(736-761)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3067707
Gieβen CWitt C(2017)The Interplay of Population Size and Mutation Probability in the ($$1+\lambda $$1+ź) EA on OneMaxAlgorithmica10.1007/s00453-016-0214-z78:2(587-609)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s00453-016-0214-z
Doerr BDoerr CNeumann FSutton A(2016)Theory for Non-TheoreticiansProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2926982(463-482)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2926982
Doerr BDoerr CYang JNeumann FSutton A(2016)Optimal Parameter Choices via Precise Black-Box AnalysisProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908950(1123-1130)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908950
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