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MGGA: Genetic algorithm with mutant genes by Tetsuyuki Takahama; Setsuko Sakai; Yoshinori Isomichi is a Computer Science article available to read on EtoBox.

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## Abstract In this research, we propose a new genetic algorithm called MGGA (Genetic Algorithm with Mutant Genes), in which mutant genes are introduced actively. An individual with mutant genes may express the phenotype of the genes imperfectly, or may even fail to express the phenotype. To realize this phenomenon, we give a new mapping function, which depends on mutant genes, from genotype to phenotype. We also introduce irreversible changes from a normal gene into a mutant gene. We can reduce the number of genes that have lower effectiveness by these irreversible changes. Through structural learning of a polynomial model and layered neural networks, we show that MGGA can optimize the parameter structure of the models and is a general‐purpose method for structural learning. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(14): 23–33, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10179

Who reads MGGA: Genetic algorithm with mutant genes?

Author

Tetsuyuki Takahama; Setsuko Sakai; Yoshinori Isomichi

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0882-1666)

Published

2002

Language

EN

Field

Computer Science (Physical Sciences)