Título
Semantic Genetic Programming Operators Based on Projections in the Projections in the Phenotype Space
Autor
ERIC SADIT TELLEZ AVILA
SABINO MIRANDA JIMENEZ
MARIO GRAFF GUERRERO
Elio Atenógenes Villaseñor García
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
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.
Editor
Research in Computing Science 94
Fecha de publicación
2015
Tipo de publicación
Artículo
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Investigadores
Repositorio Orígen
Repositorio Institucional de INFOTEC
Descargas
328