Transgenic: An evolutionary algorithm operator

Autor(es)

do Amaral, Laurence Rodrigues ^{[1, 2]} ; Hruschka, Jr., Estevam Rafael ^[1]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Dept Comp Sci, BR-13565905 Sao Carlos, SP - Brazil

^[2] Univ Fed Uberlandia, Fac Computat, BR-38700103 Patos De Minas, MG - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Neurocomputing; v. 127, p. 104-113, MAR 15 2014.
• Citações Web of Science: 1

Resumo

Traditionally, many evolutionary algorithm operators have biological inspiration. Genetics has contributed to the proposal of a number of different evolutionary operators, such as haploid crossover, mutation, diploid, inversion, gene doubling, deletion, and others. In the present study, we propose a new genetic-inspired evolutionary operator, named Transgenic, which was specially designed for Genetic Algorithms (GA). The proposed operator is inspired by genetically modified organisms (GMOs), where important features are artificially introduced into their genome. Transgenic can be used to artificially insert relevant characteristics in the chromosome of individuals, thus converging to better results faster than traditional GAs. When relevant characteristics are known a prior, then, Transgenic simply forces the presence of such characteristics in part of the population (in an elitism-based approach). Whenever there is no a priori knowledge available, Transgenic automatically identifies relevant features (based on historical information) to perform the elitism approach. The GA, used in this study was designed to allow the discovery of concise, yet accurate, high-level rules (from synthetic and real biological databases) which can be used as a classification system. The empirical results have shown that Transgenic is capable of generating better results than traditional rule classification methods, such as J48, Single Conjunctive Rule Learner, One R and PART, using synthetic datasets. (C) 2013 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 13/07787-6 - Modelos e algoritmos para o aprendizado sem fim
• Beneficiário: Estevam Rafael Hruschka Júnior
• Modalidade de apoio: Auxílio à Pesquisa - Regular