article

Performance analysis of randomised search heuristics operating with a fixed budget

Authors:

Christine ZargesAuthors Info & Claims

Theoretical Computer Science, Volume 545

Pages 39 - 58

https://doi.org/10.1016/j.tcs.2013.06.007

Published: 01 August 2014 Publication History

Abstract

When for a difficult real-world optimisation problem no good problem-specific algorithm is available often randomised search heuristics are used. They are hoped to deliver good solutions in acceptable time. The theoretical analysis usually concentrates on the average time needed to find an optimal or approximately optimal solution. This matches neither the application in practice nor the empirical analysis since usually optimal solutions are not known and even if found cannot be recognised. More often the algorithms are stopped after some time. This motivates a theoretical analysis to concentrate on the quality of the best solution obtained after a pre-specified number of function evaluations called budget. Using this perspective two simple randomised search heuristics, random local search and the (1+1) evolutionary algorithm, are analysed on some well-known example problems. Upper and lower bounds on the expected quality of a solution for a fixed budget of function evaluations are proven. The analysis shows novel and challenging problems in the study of randomised search heuristics. It demonstrates the potential of this shift in perspective from expected run time to expected solution quality.

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Doerr, B., Jansen, T., Witt, C. and Zarges, C., A method to derive fixed budget results from expected optimisation times. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2013), ACM Press.

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Cited By

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cover image Theoretical Computer Science

Theoretical Computer Science Volume 545, Issue

August, 2014

128 pages

ISSN:0304-3975

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Copyright © Elsevier B.V. © 2013.

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Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 August 2014

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Cited By

Doerr BLi XHandl J(2024)A Gentle Introduction to Theory (for Non-Theoreticians)Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3648402(800-829)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3648402
Doerr BKelley A(2024)Fourier Analysis Meets Runtime Analysis: Precise Runtimes on PlateausAlgorithmica10.1007/s00453-024-01232-586:8(2479-2518)Online publication date: 1-Aug-2024
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Doerr BSilva SPaquete L(2023)A Gentle Introduction to Theory (for Non-Theoreticians)Proceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3595042(946-975)Online publication date: 15-Jul-2023
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Vinokurov DBuzdalov MWagner M(2022)On optimal static and dynamic parameter choices for fixed-target optimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528875(876-883)Online publication date: 8-Jul-2022
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Doerr BHadri OPinard AWagner M(2022)The (1 + (λ, λ)) global SEMO algorithmProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528868(520-528)Online publication date: 8-Jul-2022
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Vinokurov DBuzdalov M(2022)Towards Fixed-Target Black-Box Complexity AnalysisParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14721-0_42(600-611)Online publication date: 10-Sep-2022
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Doerr BNeumann F(2021)A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete OptimizationACM Transactions on Evolutionary Learning and Optimization10.1145/34723041:4(1-43)Online publication date: 13-Oct-2021
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