Abstract
Recently a mechanism called stagnation detection was proposed that automatically adjusts the mutation rate of evolutionary algorithms when they encounter local optima. The so-called SD-(1+1) EA introduced by Rajabi and Witt (GECCO 2020) adds stagnation detection to the classical (1+1) EA with standard bit mutation, which flips each bit independently with some mutation rate, and raises the mutation rate when the algorithm is likely to have encountered local optima. In this paper, we investigate stagnation detection in the context of the k-bit flip operator of randomized local search that flips k bits chosen uniformly at random and let stagnation detection adjust the parameter k. We obtain improved runtime results compared to the SD-(1+1) EA amounting to a speed-up of up to e= 2.71 ⋯ Moreover, we propose additional schemes that prevent infinite optimization times even if the algorithm misses a working choice of k due to unlucky events. Finally, we present an example where standard bit mutation still outperforms the local k-bit flip with stagnation detection.
Original language | English |
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Title of host publication | Evolutionary Computation in Combinatorial Optimization |
Editors | Christine Zarges, Sébastien Verel |
Publisher | Springer |
Publication date | 2021 |
Pages | 152-168 |
ISBN (Print) | 9783030729035 |
DOIs | |
Publication status | Published - 2021 |
Event | 21st European Conference on Evolutionary Computation in Combinatorial Optimization - Virtual, Online Duration: 7 Apr 2021 → 9 Apr 2021 http://www.evostar.org/2021/evocop/ |
Conference
Conference | 21st European Conference on Evolutionary Computation in Combinatorial Optimization |
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City | Virtual, Online |
Period | 07/04/2021 → 09/04/2021 |
Internet address |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12692 LNCS |
ISSN | 0302-9743 |
Bibliographical note
Funding Information:Supported by a grant from the Danish Council for Independent Research (DFF-FNU 8021-00260B).
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
Keywords
- Local search
- Multimodal functions
- Randomized search heuristics
- Runtime analysis
- Self-adjusting algorithms