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Biased random-key genetic algorithm with local search applied to the maximum diversity problem

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Resumo:The maximum diversity problem (MDP) aims to select a subset with a predetermined number of elements from a given set, maximizing the diversity among them. This NP-hard problem requires efficient algorithms that can generate high-quality solutions within reasonable computa tional time. In this study, we propose a novel approach that combines the biased random-key genetic algorithm (BRKGA) with local search to tackle the MDP. Our computational study utilizes a com prehensive set of MDPLib instances, and demonstrates the superior average performance of our proposed algorithm compared to existing literature results. The MDP has a wide range of practical applications, including biology, ecology, and management. We provide future research directions for improving the algorithm’s performance and exploring its applicability in real-world scenarios.
Autores principais:Silva, Geiza
Outros Autores:Leite, André; Ospina, Raydonal; Leiva, Víctor; Figueroa-Zúñiga, Jorge; Castro, Cecília
Assunto:Biological diversity conservation Random-key genetic algorithm Evolutionary algorithms Computational simulations
Ano:2023
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

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