Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution

  1. Marrero, Alejandro 1
  2. Segredo, Eduardo 1
  3. León, Coromoto 1
  4. Hart, Emma 2
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

  2. 2 Edinburgh Napier University, Edinburgh, United Kingdom
Aktak:
GECCO 24: Proceedings of the Genetic and Evolutionary Computation Conference

Argitalpen urtea: 2024

Orrialdeak: 206-213

Mota: Biltzar ekarpena

DOI: 10.1145/3638529.3654028 GOOGLE SCHOLAR lock_openSarbide irekia editor

Laburpena

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.

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Finantzaketari buruzko informazioa

Finantzatzaile

  • EPSRC
    • EP/V026534/1
  • Canarian Agency for Research, Innovation and Information Society of the Department of Universities, Science and Innovation
  • 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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