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Run-Time Analysis of Population-Based Evolutionary Algorithm in Noisy Environments

Authors:

Adam Prugel-Bennett,

Jonathan Rowe,

Jonathan ShapiroAuthors Info & Claims

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

Pages 69 - 75

https://doi.org/10.1145/2725494.2725498

Published: 17 January 2015 Publication History

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Abstract

This paper analyses a generational evolutionary algorithm using only selection and uniform crossover. With a probability arbitrarily close to one the evolutionary algorithm is shown to solve onemax in O(n log²(n)) function evaluations using a population of size c,n, log(n). We then show that this algorithm can solve onemax with noise variance n again in O(n log²(n)) function evaluations.

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Cited By

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Lengler JSchiller LSieberling OLi XHandl J(2024)Plus Strategies are Exponentially Slower for Planted Optima of Random HeightProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654088(1587-1595)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654088
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
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
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Index Terms

Run-Time Analysis of Population-Based Evolutionary Algorithm in Noisy Environments
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

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

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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Published: 17 January 2015

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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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Lengler JSchiller LSieberling OLi XHandl J(2024)Plus Strategies are Exponentially Slower for Planted Optima of Random HeightProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654088(1587-1595)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654088
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
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
Friedrich TKötzing TNeumann FRadhakrishnan A(2022)Theoretical Study of Optimizing Rugged Landscapes with the cGAParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14721-0_41(586-599)Online publication date: 10-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-14721-0_41
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
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Lehre PQin XKrawiec K(2021)More precise runtime analyses of non-elitist EAs in uncertain environmentsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459312(1160-1168)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459312
Bian CQian CYu YTang K(2021)On the robustness of median sampling in noisy evolutionary optimizationScience China Information Sciences10.1007/s11432-020-3114-y64:5Online publication date: 8-Apr-2021
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Sudholt D(2020)Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is BeneficialAlgorithmica10.1007/s00453-020-00671-0Online publication date: 25-Jan-2020
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Qian CBian CYu YTang KYao X(2020)Analysis of Noisy Evolutionary Optimization When Sampling FailsAlgorithmica10.1007/s00453-019-00666-6Online publication date: 20-Jan-2020
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Real royal road functions for constant population size

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

Run-time analysis of the (1+1) evolutionary algorithm optimizing linear functions over a finite alphabet

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Real royal road functions for constant population size

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

Run-time analysis of the (1+1) evolutionary algorithm optimizing linear functions over a finite alphabet

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