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DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains

Marrero, Alejandro; Segredo, Eduardo; León, Coromoto; Hart, Emma

Authors

Alejandro Marrero

Eduardo Segredo

Coromoto León



Abstract

To advance research in the development of optimisation algorithms, it is crucial to have access to large test-beds of diverse and discriminatory instances from a domain that can highlight strengths and weaknesses of different algorithms. The DIGNEA tool enables diverse instance suites to be generated for any domain, that are also discriminatory with respect to a set of solvers of the user choice. Written in C++, and delivered as a repository and as a Docker image, its modular and template-based design enables it to be easily adapted to multiple domains and types of solvers with minimal effort. This paper exemplifies how to generate instances for the Knapsack Problem domain.

Journal Article Type Article
Acceptance Date Mar 6, 2023
Online Publication Date Mar 15, 2023
Publication Date 2023-05
Deposit Date Mar 31, 2023
Publicly Available Date Mar 31, 2023
Journal SoftwareX
Print ISSN 2352-7110
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 22
Article Number 101355
DOI https://doi.org/10.1016/j.softx.2023.101355
Keywords Instance generation, Novelty search, Evolutionary algorithm, Optimisation, Knapsack problem

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