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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Bibliographic Details
Published inAnnals of mathematics and artificial intelligence Vol. 90; no. 7-9; pp. 715 - 734
Main Authors Ansótegui, Carlos, Pon, Josep, Sellmann, Meinolf
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.09.2022
Springer
Springer Nature B.V
Subjects
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
Online AccessGet full text
ISSN1012-2443
1573-7470
DOI10.1007/s10472-020-09726-y

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Summary: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.
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ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-020-09726-y