research-article

Evolutionary algorithms and artificial immune systems on a bi-stable dynamic optimisation problem

Authors:

Christine ZargesAuthors Info & Claims

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 975 - 982

https://doi.org/10.1145/2576768.2598344

Published: 12 July 2014 Publication History

Abstract

Dynamic optimisation is an important area of application for evolutionary algorithms and other randomised search heuristics. Theoretical investigations are currently far behind practical successes. Addressing this deficiency a bi-stable dynamic optimisation problem is introduced and the performance of standard evolutionary algorithms and artificial immune systems is assessed. Deviating from the common theoretical perspective that concentrates on the expected time to find a global optimum (again) here the `any time performance' of the algorithms is analysed, i.e., the expected function value at each step. Basis for the analysis is the recently introduced perspective of fixed budget computations. Different dynamic scenarios are considered which are characterised by the length of the stable phases. For each scenario different population sizes are examined. It is shown that the evolutionary algorithms tend to have superior performance in almost all cases.

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Cited By

Wang CHe JChen YZou X(2022)Influence of Binomial Crossover on Approximation Error of Evolutionary AlgorithmsMathematics10.3390/math1016285010:16(2850)Online publication date: 10-Aug-2022
https://doi.org/10.3390/math10162850
Lissovoi AWitt C(2018)The Impact of a Sparse Migration Topology on the Runtime of Island Models in Dynamic OptimizationAlgorithmica10.1007/s00453-017-0377-280:5(1634-1657)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s00453-017-0377-2
Kötzing TKrejca M(2018)First-Hitting Times for Finite State SpacesParallel Problem Solving from Nature – PPSN XV10.1007/978-3-319-99259-4_7(79-91)Online publication date: 21-Aug-2018
https://doi.org/10.1007/978-3-319-99259-4_7
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Index Terms

Evolutionary algorithms and artificial immune systems on a bi-stable dynamic optimisation problem
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1478 pages

ISBN:9781450326629

DOI:10.1145/2576768

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Copyright © 2014 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 2014

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

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

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Cited By

Wang CHe JChen YZou X(2022)Influence of Binomial Crossover on Approximation Error of Evolutionary AlgorithmsMathematics10.3390/math1016285010:16(2850)Online publication date: 10-Aug-2022
https://doi.org/10.3390/math10162850
Lissovoi AWitt C(2018)The Impact of a Sparse Migration Topology on the Runtime of Island Models in Dynamic OptimizationAlgorithmica10.1007/s00453-017-0377-280:5(1634-1657)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s00453-017-0377-2
Kötzing TKrejca M(2018)First-Hitting Times for Finite State SpacesParallel Problem Solving from Nature – PPSN XV10.1007/978-3-319-99259-4_7(79-91)Online publication date: 21-Aug-2018
https://doi.org/10.1007/978-3-319-99259-4_7
Nallaperuma SNeumann FSudholt D(2017)Expected fitness gains of randomized search heuristics for the traveling salesperson problemEvolutionary Computation10.1162/evco_a_0019925:4(673-705)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1162/evco_a_00199
Lissovoi AWitt C(2017)A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic OptimizationAlgorithmica10.1007/s00453-016-0262-478:2(641-659)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s00453-016-0262-4
Dang DJansen TLehre P(2017)Populations Can Be Essential in Tracking Dynamic OptimaAlgorithmica10.1007/s00453-016-0187-y78:2(660-680)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s00453-016-0187-y
Lissovoi AWitt CNeumann FSutton A(2016)The Impact of Migration Topology on the Runtime of Island Models in Dynamic OptimizationProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908843(1155-1162)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908843
Lissovoi AWitt C(2016)MMAS Versus Population-Based EA on a Family of Dynamic Fitness FunctionsAlgorithmica10.1007/s00453-015-9975-z75:3(554-576)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s00453-015-9975-z
Neumann FWitt C(2015)On the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan schedulingProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832747.2832771(3742-3748)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832747.2832771
Jansen TZarges C(2015)Analysis of randomised search heuristics for dynamic optimisationEvolutionary Computation10.1162/EVCO_a_0016423:4(513-541)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1162/EVCO_a_00164
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