Alejandro Marrero
Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution
Marrero, Alejandro; Segredo, Eduardo; León, Coromoto; Hart, Emma
Abstract
The ability to generate example instances from a domain is important in order to benchmark algorithms and to generate data that covers an instance-space in order to train machine-learning models for algorithm selection. Quality-Diversity (QD) algorithms have recently been shown to be effective in generating diverse and discriminatory instances with respect to a portfolio of solvers in various combinatorial optimisation domains. However these methods all rely on defining a descriptor which defines the space in which the algorithm searches for diversity: this is usually done manually defining a vector of features relevant to the domain. As this is a limiting factor in the use of QD methods, we propose a meta-QD algorithm which uses an evolutionary algorithm to search for a nonlinear 2D projection of an original feature-space such that applying novelty-search method in this space to generate instances improves the coverage of the instance-space. We demonstrate the effectiveness of the approach by generating instances from the Knapsack domain, showing the meta-QD approach both generates instances in regions of an instance-space not covered by other methods, and also produces significantly more instances.
Citation
Marrero, A., Segredo, E., León, C., & Hart, E. (in press). Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. In Genetic and Evolutionary Computation Conference (GECCO ’24), July 14–18, 2024, Melbourne, VIC, Australia. https://doi.org/10.1145/3638529.3654028
Conference Name | ACM GECCO 2024 |
---|---|
Conference Location | Melbourne, Australia |
Start Date | Jul 14, 2024 |
End Date | Jul 18, 2024 |
Acceptance Date | Mar 21, 2024 |
Deposit Date | Apr 17, 2024 |
Publisher | Association for Computing Machinery (ACM) |
Book Title | Genetic and Evolutionary Computation Conference (GECCO ’24), July 14–18, 2024, Melbourne, VIC, Australia |
ISBN | 9798400704949 |
DOI | https://doi.org/10.1145/3638529.3654028 |
Keywords | Instance generation, instance-space analysis, knapsack problem, novelty search, evolutionary computation, neural-network |
Public URL | http://researchrepository.napier.ac.uk/Output/3594757 |
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Contact repository@napier.ac.uk to request a copy for personal use.
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