Abordagem neuro-genética para mapeamento de problemas de conexão em otimização combinatória / Neurogenetic approach for mapping connection problems in combinatorial optimization

AUTOR(ES)
DATA DE PUBLICAÇÃO

2009

RESUMO

Due to applicability constraints involved with the algorithms for solving combinatorial optimization problems, systems based on artificial neural networks and genetic algorithms are alternative methods for solving these problems in an efficient way. The genetic algorithms must its popularity to make possible cover nonlinear and extensive search spaces. On the other hand, 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. Additionally, 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 connection problems in combinatorial optimization using a neurogenetic 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 combinatorial optimization problems.

ASSUNTO(S)

artificial neural networks problema do emparelhamento bipartido n-queens problem redes neurais artificiais problema das n-rainhas genetic algorithms bipartite graph optimization otimização combinatória shortest path problem algoritmos genéticos combinatorial optimization problema do caminho mínimo

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