Hybridized Neural Network and Genetic Algorithms for Solving Nonlinear Integer Programming Problem

Authors:
Mitsuo Gen;Kenichi Ida;Chang-Yun Lee
Affiliations:
-;-;-
Venue:
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
Year:
1998

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Abstract

Optimization problems such as system reliability design and general assignment problem are generally formulated as a nonlinear integer programming (NIP) problem. Generally, we transform the nonlinear integer programming problem into a linear programming one in order to solve NIP problems. However linear programming problems transformed from NIP problems become a large-scale problem. In principal, it is desired that we deal with the NIP problems without any transformation. In this paper, we propose a new method in which a neural network technique is hybridized with genetic algorithms for solving nonlinear integer programming problems. The hybrid GA is employed the simpelx search method, and the chromosomes are improved to good points by using the simplex search method. The effectiveness and efficiency of this approach are shown with numerical simulations from the reliability optimal design problem.