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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.)

Studying bloat control and maintenance of effective code in linear genetic programming for symbolic regression

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Autor(es):
dal Piccol Sotto, Leo Francoso [1] ; de Melo, Vinicius Veloso [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Fed Univ Sao Paulo Unifesp, Inst Sci & Technol ICT, Sao Jose Dos Campos, SP - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: Neurocomputing; v. 180, n. SI, p. 79-93, MAR 5 2016.
Citações Web of Science: 5
Resumo

Linear Genetic Programming (LGP) is an Evolutionary Computation algorithm, inspired in the Genetic Programming (GP) algorithm. Instead of using the standard tree representation of GP, LGP evolves a linear program, which causes a graph-based data flow with code reuse. LGP has been shown to outperform GP in several problems, including Symbolic Regression (SReg), and to produce simpler solutions. In this paper, we propose several LGP variants and compare them with a traditional LGP algorithm on a set of benchmark SReg functions from the literature. The main objectives of the variants were to both control bloat and privilege useful code in the population. Here we evaluate their effects during the evolution process and in the quality of the final solutions. Analysis of the results showed that bloat control and effective code maintenance worked, but they did not guarantee improvement in solution quality. (C) 2015 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 13/20606-0 - Evolução automática de árvores de comportamento para um agente inteligente
Beneficiário:Léo Françoso Dal Piccol Sotto
Linha de fomento: Bolsas no Brasil - Iniciação Científica