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Efficient Optimisation of Noisy Fitness Functions with Population-based Evolutionary Algorithms

Authors:

Duc-Cuong Dang,

Per Kristian LehreAuthors Info & Claims

FOGA '15: Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII

Pages 62 - 68

https://doi.org/10.1145/2725494.2725508

Published: 17 January 2015 Publication History

Abstract

Population-based EAs can optimise pseudo-Boolean functions in expected polynomial time, even when only partial information about the problem is available [7]. In this paper, we show that the approach used to analyse optimisation with partial information extends naturally to optimisation under noise. We consider pseudo-Boolean problems with an additive noise term. Very general conditions on the noise term is derived, under which the EA optimises the noisy function in expected polynomial time. In the case of the Onemax and Leadingones problems, efficient optimisation is even possible when the variance of the noise distribution grows quickly with the problem size.

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Cited By

Dang DLehre PLi XHandl J(2024)The SLO Hierarchy of pseudo-Boolean Functions and Runtime of Evolutionary AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654221(1551-1559)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654221
Dang DOpris ASudholt D(2024)Level-Based Theorems for Runtime Analysis of Multi-objective Evolutionary AlgorithmsParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_16(246-263)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70071-2_16
Aishwaryaprajna Rowe J(2023)Evolutionary and Estimation of Distribution Algorithms for Unconstrained, Constrained, and Multiobjective Noisy Combinatorial Optimisation ProblemsEvolutionary Computation10.1162/evco_a_0032031:3(259-285)Online publication date: 1-Sep-2023
https://doi.org/10.1162/evco_a_00320
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Index Terms

Efficient Optimisation of Noisy Fitness Functions with Population-based Evolutionary Algorithms
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

FOGA '15: Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII

January 2015

200 pages

ISBN:9781450334341

DOI:10.1145/2725494

Program Chairs:
Jun He
Aberystwyth University, UK
,
Thomas Jansen
Aberystwyth University, UK
,
Gabriela Ochoa
University of Stirling, UK
,
Christine Zarges
University of Birmingham, UK

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 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: 17 January 2015

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Seventh Framework Programme

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FOGA '15

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SIGEVO

FOGA '15: Foundations of Genetic Algorithms XIII

January 17 - 22, 2015

Aberystwyth, United Kingdom

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FOGA '15 Paper Acceptance Rate 16 of 26 submissions, 62%;

Overall Acceptance Rate 72 of 131 submissions, 55%

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Cited By

Dang DLehre PLi XHandl J(2024)The SLO Hierarchy of pseudo-Boolean Functions and Runtime of Evolutionary AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654221(1551-1559)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654221
Dang DOpris ASudholt D(2024)Level-Based Theorems for Runtime Analysis of Multi-objective Evolutionary AlgorithmsParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_16(246-263)Online publication date: 7-Sep-2024
https://doi.org/10.1007/978-3-031-70071-2_16
Aishwaryaprajna Rowe J(2023)Evolutionary and Estimation of Distribution Algorithms for Unconstrained, Constrained, and Multiobjective Noisy Combinatorial Optimisation ProblemsEvolutionary Computation10.1162/evco_a_0032031:3(259-285)Online publication date: 1-Sep-2023
https://doi.org/10.1162/evco_a_00320
Lehre PQin X(2023)Self-adaptation Can Improve the Noise-tolerance of Evolutionary AlgorithmsProceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms10.1145/3594805.3607128(105-116)Online publication date: 30-Aug-2023
https://dl.acm.org/doi/10.1145/3594805.3607128
Dang DOpris ASalehi BSudholt DSilva SPaquete L(2023)Analysing the Robustness of NSGA-II under NoiseProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590421(642-651)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590421
Tonda AAlvarez IMartin SSquillero GLutton ESilva SPaquete L(2023)Towards Evolutionary Control Laws for Viability ProblemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590415(1464-1472)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590415
Aishwaryaprajna Rowe J(2023)The Voting Algorithm is Robust to Various Noise ModelsTheoretical Computer Science10.1016/j.tcs.2023.113844(113844)Online publication date: Mar-2023
https://doi.org/10.1016/j.tcs.2023.113844
Lehre PQin X(2022)More Precise Runtime Analyses of Non-elitist Evolutionary Algorithms in Uncertain EnvironmentsAlgorithmica10.1007/s00453-022-01044-586:2(396-441)Online publication date: 2-Oct-2022
https://doi.org/10.1007/s00453-022-01044-5
Qin XLehre P(2022)Self-adaptation via Multi-objectivisation: An Empirical StudyParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14714-2_22(308-323)Online publication date: 14-Aug-2022
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Diochnos D(2021)On the Evolvability of Monotone Conjunctions with an Evolutionary Mutation MechanismJournal of Artificial Intelligence Research10.1613/jair.1.1205070(891-921)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1613/jair.1.12050
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