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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

A framework for inducing artificial changes in optimization problems

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Autor(es):
Tinos, Renato [1] ; Yang, Shengxiang [2]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Dept Comp & Math, FFCLRP, BR-14040901 Ribeirao Preto, SP - Brazil
[2] De Montfort Univ, CCI, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics - England
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: INFORMATION SCIENCES; v. 485, p. 486-504, JUN 2019.
Citações Web of Science: 0
Resumo

Environmental changes are traditionally considered intrinsic in evolutionary dynamic optimization. However, by ignoring that changes can instead be induced, we are ignoring that environmental changes can be eventually beneficial. To investigate the impact of artificial changes on the optimization speed up, we propose a framework for inducing artificial changes in any pseudo-Boolean or continuous optimization in this paper. Seven types of changes can be induced. Knowing when and how the changes occur allows us to design new strategies for evolutionary algorithms. Through computational experiments and illustrative examples, the impact of introducing changes in the optimization process is investigated. Experimental results indicate that changing the environments according to the proposed framework can lead to higher speed up, but not for all problems and change types. The best performance was obtained by change types that introduce plateaus and/or modify the gradient of regions of the fitness landscape around the current best solution. By doing this, the evolutionary dynamics is modified, eventually allowing the population to escape faster from local optima and reach new zones of the fitness landscape. Given a pseudo-Boolean or continuous optimization static problem, the proposed framework can be used to dynamically change the problem to speed up the optimization. (C) 2019 Elsevier Inc. All rights reserved. (AU)

Processo FAPESP: 16/18615-0 - Aprendizado de máquina avançado
Beneficiário:André Carlos Ponce de Leon Ferreira de Carvalho
Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
Processo FAPESP: 15/06462-1 - Recombinação por decomposição em computação evolutiva
Beneficiário:Renato Tinós
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
Beneficiário:José Alberto Cuminato
Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs