Title

Semantic Genetic Programming Operators Based on Projections in the Projections in the Phenotype Space

Author

ERIC SADIT TELLEZ AVILA

SABINO MIRANDA JIMENEZ

MARIO GRAFF GUERRERO

Elio Atenógenes Villaseñor García

Access level

Open Access

Summary or description

In the Genetic Programming (GP) community there has been a great interest in developing semantic genetic operators. These type of operators use information of the phenotype to create ospring. The most recent approaches of semantic GP include the GP framework based on the alignment of error space, the geometric semantic genetic operators, and backpropagation genetic operators. Our contribution proposes two semantic operators based on projections in the phenotype space. The proposed operators have the characteristic, by construction, that the ospring's tness is as at least as good as the tness of the best parent; using as tness the euclidean distance. The semantic operators proposed increment the learning capabilities of GP. These operators are compared against a traditional GP and Geometric Semantic GP in the Human oral bioavailability regression problem and 13 classication problems. The results show that a GP system with our novel semantic operators has the best performance in the training phase in all the problems tested.

Publisher

Research in Computing Science 94

Publish date

2015

Publication type

Article

Format

application/pdf

Language

English

Audience

Researchers

Source repository

Repositorio Institucional de INFOTEC

Downloads

153

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