Título
Selection Heuristics on Semantic Genetic Programming for Classification Problems
Autor
CLAUDIA NALLELY SANCHEZ GOMEZ
Colaborador
MARIO GRAFF GUERRERO (Asesor de tesis)
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
In this dissertation, three heuristics for parent selection in Genetic Programming (GP) based on functions’ properties and individuals’ semantics have been proposed. Semantics have recently used for guiding the learning process when GP is used to solve supervised learning problems. However, to the best of our knowledge, this is the first time that functions’ properties are used for guiding the learning process in GP. The Pheuristics are tailored to the function , and the classifiers Naive Bayes and Nearest Centroid. The first heuristic based on cosine similarity promotes the selection of parents whose semantics are as perpendicular as possible between them in the semantics space. The second one, based on Pearson’s correlation coefficient, searches parents whose semantics’ vectors are uncorrelated. Finally, the last one, based on accuracy, tries to select parents whose semantics are different between them. In addition, we analyze the use of completely random selection for parents and negative selection. These selection techniques were implemented on EvoDAG, a Semantic Genetic Programming system. For comparing the different selection schemes, we use 30 classification problems with a variable number of samples, variables, and classes. The results indicate that the combination of our heuristic based on accuracy for parent selection and negative random selection produces the best combination, and the difference in performances between this combination and the classical selection based on fitness is statistically significant. Furthermore, we compare our heuristics with state-of-the-art schemes, Angle-Driven Selection, and Novelty Search. Besides, EvoDAG with the proposed selection heuristics was compared against 18 classifiers that included traditional approaches as well as auto-machine-learning techniques. We conclude that the use of our proposed heuristics significantly improves the learning process of EvoDAG.
Editor
INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación
Fecha de publicación
julio de 2020
Tipo de publicación
Otro
Trabajo de grado, doctorado
Versión de la publicación
Versión publicada
Recurso de información
Formato
application/pdf
Idioma
Español
Sugerencia de citación
Claudia Nallely Sánchez Gómez, 2020. Selection Heuristics on Semantic Genetic Programming for Classification Problems. INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación, Ciudad de México.
Repositorio Orígen
Repositorio Institucional de INFOTEC
Descargas
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