Neural networks (NNs) and genetic algorithms (GAs) are the two most popular bio-inspired techniques. Criticism of these approaches includes the tendency of recurrent neural networks to produce infeasible solutions, the lack of generalize of the self-organizing approaches, and the requirement of tuning many internal parameters and operators of genetic algorithms. This paper proposes a new technique which enables feasible solutions, removes the tuning phase, and improves solutions quality of typical combinatorial optimization problems as the p-median problem. Moreover, several biology inspired approaches are analyzed for solving traditional benchmarks.
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