Boosting evolutionary algorithm configuration

Algorithm configuration has emerged as an essential technology for the improvement of high-performance solvers. We present new algorithmic ideas to improve state-of-the-art solver configurators automatically by tuning. Particularly, we introduce 1. a forward-simulation method to improve parallel per...

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Vydáno v:Annals of mathematics and artificial intelligence Ročník 90; číslo 7-9; s. 715 - 734
Hlavní autoři: Ansótegui, Carlos, Pon, Josep, Sellmann, Meinolf
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.09.2022
Springer
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Business metrics
Combinatorial analysis
Complex Systems
Computer Science
Configurations
Evolutionary algorithms
Genetic algorithms
Logic programming
Mathematical programming
Mathematics
Optimization
Solvers
State of the art
Tournaments & championships
Algorithm portfolios
68
Algorithm configuration
Genetic algorithms
ISSN:1012-2443, 1573-7470
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Shrnutí:Algorithm configuration has emerged as an essential technology for the improvement of high-performance solvers. We present new algorithmic ideas to improve state-of-the-art solver configurators automatically by tuning. Particularly, we introduce 1. a forward-simulation method to improve parallel performance, 2. an improvement to the configuration process itself, and 3. a new technique for instance-specific solver configuration. Extensive experimental results show that the new solver configurator compares very favorably with the state-of-the-art in automatic configuration for combinatorial solvers.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-020-09726-y