Aplicacao de uma Programacao Genetica Gramatical Coevolutiva no Apoio a Inferencia da Maxima Deformacao Longitudinal de dutos com Amassamento

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Misc{joao2017aplicacao,

• author = "Joao Macros {de Freitas}",
• title = "Aplicacao de uma Programacao Genetica Gramatical Coevolutiva no Apoio a Inferencia da Maxima Deformacao Longitudinal de dutos com Amassamento",
• school = "Ciencia da Computacao, Federal University of Juiz de Fora",
• year = "2016",
• type = "Bachelor in computer science",
• address = "Brazil",
• month = "9 " # dec,
• keywords = "genetic algorithms, genetic programming, Grammar based Genetic Programming, Co-evolution, Computational Intelligence, Pipe Engineering",
• URL = "http://monografias.ice.ufjf.br/tcc-web/tcc?id=266",
• size = "44 pages",
• abstract = "The extraction of underground fluid fuels is an important resource in order to produce energy. However, there are several factors that make this practice hard, such as damage that causes deformations on the pipe that extracts the fuels. The objective of this work is to determine relationships between characteristics observed on the pipe and fluid with the maximum longitudinal deformation of the pipe from data obtained through analyses using the Finite Element Method. An automatic process for knowledge discovery using an intelligent system that can evolve models in symbolic form is proposed here. Genetic Programming methods presented good results to this type of application and a grammatical approach is adopted here, where the models (programs) are inferred by means of a Formal Grammar. A grammar brings to the GP technique the benefit of generating only valid programs/models and the possibility of limiting the search space by introducing bias. Also, a coevolutionary approach is used to focus the search process on data which are harder to evaluate. Preliminary computational experiments were conducted to solve the problem of inferring a model of the maximum longitudinal deformation of pipes and the results indicate that the application of a Co-evolutionary Grammar based Genetic Programming can solve this problem with good accuracy and less computational cost.",
• key = "FREITAS,2017",
• notes = "In Portuguese

  Supervisor: Heder Soares Bernardino",

}

Genetic Programming entries for Joao Macros de Freitas

Citations