Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution
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Marrero, Alejandro
1
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Segredo, Eduardo
1
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León, Coromoto
1
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Hart, Emma
2
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1
Universidad de La Laguna
info
- 2 Edinburgh Napier University, Edinburgh, United Kingdom
Año de publicación: 2024
Páginas: 206-213
Tipo: Aportación congreso
Resumen
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.
Información de financiación
Financiadores
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EPSRC
- EP/V026534/1
- Canarian Agency for Research, Innovation and Information Society of the Department of Universities, Science and Innovation
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Culture and by the European Social Fund Plus (ESF+) Integrated Operational Program of the Canary Islands 2021-2027, Axis 3 Priority Topic 74 (85%)
- TESIS2020010005
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