research-article

Evaluation of heavy-tailed mutation operator on maximum flow test generation problem

Authors:

Vladimir Mironovich,

Maxim BuzdalovAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1423 - 1426

https://doi.org/10.1145/3067695.3082507

Published: 15 July 2017 Publication History

Abstract

The general recommendation for the mutation rate in standard-bit mutation is 1/n, which gives asymptotically optimal expected optimization times for several simple test problems. Recently, Doerr et al. have shown that such mutation rate is not ideal, and is far from optimal for multimodal problems. They proposed the heavy-tailed mutation operator fmut_β which significantly improves performance of the (1+1) evolutionary algorithm on Jump problem and yields similar speed-ups for the vertex cover problem in bipartite graphs and the matching problem in general graphs.

We evaluate the fmut_β mutation operator on the problem of hard test generation for the maximum flow algorithms. Experiments show that the fmut_β mutation operator greatly increases performance of the (1+1) evolutionary algorithm. It also achieves performance improvement, although less drastic, on a simple population based algorithm, but hinders performance of a crossover based genetic algorithm.

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Maxim Buzdalov and Anatoly Shalyto. 2015. Hard Test Generation for Augmenting Path Maximum Flow Algorithms using Genetic Algorithms: Revisited. In Proceedings of IEEE Congress on Evolutionary Computation. 2121--2128.

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Vladimir Mironovich and Maxim Buzdalov. 2015. Hard Test Generation for Maximum Flow Algorithms with the Fast Crossover-Based Evolutionary Algorithm. In Proceedings of Genetic and Evolutionary Computation Conference Companion. 1229--1232.

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Vladimir Mironovich and Maxim Buzdalov. 2016. Comparative Study of Representations in the Maximum Flow Test Generation Problem. In Proceedings of 22nd International Conference on Soft Computing MENDEL 2016. Czech Republic, 67--72.

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Cited By

Doerr CJanett DLengler J(2024)Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear FunctionsAlgorithmica10.1007/s00453-024-01258-986:10(3115-3152)Online publication date: 22-Jul-2024
https://doi.org/10.1007/s00453-024-01258-9
Doerr CJanett DLengler JSilva SPaquete L(2023)Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear FunctionsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590482(1565-1574)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590482
Doerr BQu Z(2023)A First Runtime Analysis of the NSGA-II on a Multimodal ProblemIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325055227:5(1288-1297)Online publication date: Oct-2023
https://doi.org/10.1109/TEVC.2023.3250552
Show More Cited By

Index Terms

Evaluation of heavy-tailed mutation operator on maximum flow test generation problem
1. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis
      1. Network flows
    2. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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cover image ACM Conferences

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Copyright © 2017 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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Publication History

Published: 15 July 2017

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Ministry of Education and Science of the Russian Federation

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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

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Citations

Cited By

Doerr CJanett DLengler J(2024)Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear FunctionsAlgorithmica10.1007/s00453-024-01258-986:10(3115-3152)Online publication date: 22-Jul-2024
https://doi.org/10.1007/s00453-024-01258-9
Doerr CJanett DLengler JSilva SPaquete L(2023)Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear FunctionsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590482(1565-1574)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590482
Doerr BQu Z(2023)A First Runtime Analysis of the NSGA-II on a Multimodal ProblemIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325055227:5(1288-1297)Online publication date: Oct-2023
https://doi.org/10.1109/TEVC.2023.3250552
Wang SZheng WDoerr B(2023)Choosing the Right Algorithm With Hints From Complexity TheoryInformation and Computation10.1016/j.ic.2023.105125(105125)Online publication date: Nov-2023
https://doi.org/10.1016/j.ic.2023.105125
Kurbatov EMironovich VWagner M(2022)Evaluation of inverse selection operators on maximum flow test generation problemProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533947(61-62)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533947
Bambury HBultel ADoerr B(2022)An Extended Jump Functions Benchmark for the Analysis of Randomized Search HeuristicsAlgorithmica10.1007/s00453-022-00977-186:1(1-32)Online publication date: 23-May-2022
https://doi.org/10.1007/s00453-022-00977-1
Antipov DDoerr BKaravaev V(2022)A Rigorous Runtime Analysis of the $$(1 + (\lambda , \lambda ))$$ GA on Jump FunctionsAlgorithmica10.1007/s00453-021-00907-784:6(1573-1602)Online publication date: 15-Jan-2022
https://doi.org/10.1007/s00453-021-00907-7
Antipov DDoerr B(2021)Precise Runtime Analysis for Plateau FunctionsACM Transactions on Evolutionary Learning and Optimization10.1145/34698001:4(1-28)Online publication date: 13-Oct-2021
https://dl.acm.org/doi/10.1145/3469800
Buzdalov MDoerr CKrawiec K(2021)Optimal static mutation strength distributions for the (1 + λ) evolutionary algorithm on OneMaxProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459389(660-668)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459389
Doerr B(2020)Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative DriftEvolutionary Computation10.1162/evco_a_00283(1-25)Online publication date: 16-Nov-2020
https://doi.org/10.1162/evco_a_00283
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