A Neuro-GA approach for the maximum fuzzy clique problem

Abstract

The maximum clique problem, into which many problems have been mapped effectively, is of great importance in graph theory. A natural extension to this problem, emerging very recently in many real-life networks, is its fuzzification. The problem of finding the maximum clique in a fuzzy graph has been addressed in this paper. It has been shown here, that this problem reduces to an unconstrained quadratic 0-1 programming problem. Using a maximum neural network, along with, chaotic mutation capability of genetic algorithms, the reduced problem has been solved. Empirical studies have been done by applying the method on a gene co-expression network and on some benchmark graphs. © 2009 Springer Berlin Heidelberg.