Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space
Generating new instances via evolutionary methods is commonly used to create new benchmarking data-sets,with a focus on attempting to cover an instance-space as completely as possible. Recent approaches have exploited Quality-Diversity methods to evolve sets of instances that are both diverse and di...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Universidad de La Laguna (ULL) |
| Repositorio: | RIULL. Repositorio Institucional de la Universidad de La Laguna |
| OAI Identifier: | oai:riull.ull.es:915/40012 |
| Acesso em linha: | http://riull.ull.es/xmlui/handle/915/40012 |
| Access Level: | acceso abierto |
| Palavra-chave: | Instance generation Instance-space analysis Knapsack problem Novelty search Evolutionary computation |
| Resumo: | Generating new instances via evolutionary methods is commonly used to create new benchmarking data-sets,with a focus on attempting to cover an instance-space as completely as possible. Recent approaches have exploited Quality-Diversity methods to evolve sets of instances that are both diverse and discriminatory with respect to a portfolio of solvers, but these methods can be challenging when attempting to find diversity in a high-dimensional feature-space. Weaddress this issue by training a model based on Principal Component Analysis on existing instances to create a low-dimension projection of the high-dimension feature-vectors, and then apply Novelty Search directly in the new low-dimension space. We conduct experiments to evolve diverse and discriminatory instances of Knapsack Problems, comparing the use of Novelty Search in the original feature-space to using Novelty Search in a low-dimensional projection, and repeat over a given set of dimensions. We find that the methods are complementary: if treated as an ensemble, they collectively provide increased coverage of the space. Specifically, searching for novelty in a low-dimension space contributes 56% of the filled regions of the space, while searching directly in the feature-space covers the remaining 44%. |
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