Regressão simbólica via programação genética: um estudo de caso com modelagem geofísica

• Ano de defesa: 2006
• Autor(a) principal: Grings, Alexandre
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Ciência da Computação; Ciências Exatas e da Terra; UFU
• Palavras-chave em Português: Regressão simbólica; Programação genética; Programação da expressão gênica; Modelagem geofísica; Programação genética (Computação); Symbolic regression; Genetic programming; Gene expression programming; Geophisical modeling; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufu.br/handle/123456789/12522

Symbolic regression, which is in principal the handling of mathematical expressions for finding a function that describes a data set, was until recently carried out exclusively by humans. But now, several computational techniques of symbolic regression automatization have appeared. One of these techniques is genetic programming, a subarea of evolutive computing that uses an analogy to Darwin s evolutionary theory and some ideas from the Genetics field to develop a group of computer programs in a search for solutions to computational tasks. This work aims to test the symbolic regression capabilities of genetic programming with the objective of verifying its viability as a tool for a specific geophysical research. This research concerns phenomena that occurs in the ionosphere, the region of earth s atmosphere ionized by the action of solar rays, that play a fundamental role in telecommunications. In the course of this trial, we used two implementations of traditional genetic programming and one implementation of a variant, named gene expression programming. Problems like the one under study demand a lot of processor time and are memory consuming, therefore, the work culminates with a distributed implementation of genetic programming with the objective of accelerating the modeling process.