2026-05-31T15:15:42Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/295832026-02-03T11:08:22Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Aldana Martín, José Francisco
author
Roldán-García, María del Mar
author
Nebro-Urbaneja, Antonio Jesús
author
Aldana-Montes, José Francisco
2024-02-01T10:37:55Z
2024-02-01T10:37:55Z
2024-01-23
José F. Aldana-Martín, María del Mar Roldán-García, Antonio J. Nebro, José F. Aldana-Montes, MOODY: An ontology-driven framework for standardizing multi-objective evolutionary algorithms, Information Sciences, Volume 661, 2024, 120184, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2024.120184
https://hdl.handle.net/10630/29583
10.1016/j.ins.2024.120184
The application of semantic technologies, particularly ontologies, in the realm of multi-objective evolutionary algorithms is overlook despite their effectiveness in knowledge representation. In this paper, we introduce MOODY, an ontology specifically tailored to formalize these kinds of algorithms, encompassing their respective parameters, and multi-objective optimization problems based on a characterization of their search space landscapes. MOODY is designed to be particularly applicable in automatic algorithm configuration, which involves the search of the parameters of an optimization algorithm to optimize its performance. In this context, we observe a notable absence of standardized components, parameters, and related considerations, such as problem characteristics and algorithm configurations. This lack of standardization introduces difficulties in the selection of valid component combinations and in the re-use of algorithmic configurations between different algorithm implementations. MOODY offers a means to infuse semantic annotations into the configurations found by automatic tools, enabling efficient querying of the results and seamless integration across diverse sources through their incorporation into a knowledge graph. We validate our proposal by presenting four case studies.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Computación evolutiva
Redes neuronales (Informática)
MOODY: An ontology-driven framework for standardizing multi-objective evolutionary algorithms
journal article
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