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Doctoral Thesis
DOI
https://doi.org/10.11606/T.18.2007.tde-11122007-104053
Document
Author
Full name
Fabiana Cristina Bertoni
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2007
Supervisor
Committee
Silva, Ivan Nunes da (President)
Carvalho, André Carlos Ponce de Leon Ferreira de
Julia, Rita Maria da Silva
Morandin Junior, Orides
Teixeira, Marcelo Carvalho Minhoto
Title in Portuguese
Uma arquitetura neuro-genética para otimização não-linear restrita
Keywords in Portuguese
Algoritmos genéticos
Otimização não-linear restrita
Redes neurais
Abstract in Portuguese
Os sistemas baseados em redes neurais artificiais e algoritmos genéticos oferecem um método alternativo para solucionar problemas relacionados à otimização de sistemas. Os algoritmos genéticos devem a sua popularidade à possibilidade de percorrer espaços de busca não-lineares e extensos. As redes neurais artificiais possuem altas taxas de processamento por utilizarem um número elevado de elementos processadores simples com alta conectividade entre si. Redes neurais com conexões realimentadas fornecem um modelo computacional capaz de resolver vários tipos de problemas de otimização, os quais consistem, geralmente, da otimização de uma função objetivo que pode estar sujeita ou não a um conjunto de restrições. Esta tese apresenta uma abordagem inovadora para resolver problemas de otimização não-linear restrita utilizando uma arquitetura neuro-genética. Mais especificamente, uma rede neural de Hopfield modificada é associada a um algoritmo genético visando garantir a convergência da rede em direção aos pontos de equilíbrio factíveis que representam as soluções para o problema de otimização não-linear restrita.
Title in English
Neuro-genetic architecture for constrained nonlinear optimization
Keywords in English
Constrained nonlinear optimization
Genetic algorithms
Neural networks
Abstract in English
Systems based on artificial neural networks and genetic algorithms are an alternative method for solving systems optimization problems. The genetic algorithms must its popularity to make possible cover nonlinear and extensive search spaces. Artificial neural networks have high processing rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems, which refer to optimization of an objective function that can be subject to constraints. This thesis presents a novel approach for solving constrained nonlinear optimization problems using a neuro-genetic approach. More specifically, a modified Hopfield neural network is associated with a genetic algorithm in order to guarantee the convergence of the network to the equilibrium points, which represent feasible solutions for the constraint nonlinear optimization problem.
 
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TeseFabianaBertoni.pdf (484.74 Kbytes)
Publishing Date
2007-12-11
 
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