AGAVaPS - Adaptive Genetic Algorithm with Varying Population Size

Autor(es)

Mendes Ribeiro, Rafael Rodrigues ; Maciel, Carlos Dias ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC); v. N/A, p. 8-pg., 2022-01-01.

Resumo

Recently there is great interest in optimization, especially on meta-heuristic algorithms. Many works have proposed improvements for these algorithms for general and specific applications. In this paper the Adaptive Genetic Algorithm with Varying Population Size (AGAVaPS) is proposed, an improvement of Genetic Algorithm. On the AGAVaPS each solution has their own mutation rate and number of iterations that the solution will be in the population. The proposed optimizer is tested against six other well established optimizers on the CEC2017 single objective optimization benchmark functions considering coverage of the search space and quality of solution obtained. It is also tested for feature selection and Bayesian network structural learning. The evolution of the population size over the iterations is also analysed. The results obtained show that the AGAVaPS has a very competitive performance in both, coverage and quality of solution. (AU)

• Processo FAPESP: 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente
• Beneficiário: Marco Henrique Terra
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/23139-8 - Aprendizagem estrutural de redes bayesianas dinâmicas utilizando algoritmo evolutivo paralelo multiobjetivo
• Beneficiário: Rafael Rodrigues Mendes Ribeiro
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto