Influence of crossover and mutation on the behavior of Genetic algorithms in Mobile Ad-hoc Networks

This paper evaluates the effect of applying well known mutation and crossover operators of Genetic algorithm in Mobile Ad-hoc NETworks (MANETs). In this paper, we propose an approach to develop an algorithm for an efficient routing in MANETs. A wireless ad-hoc network can be defined by a collection of mobile nodes, connected over a wireless medium. Genetic algorithm is an evolutionary optimization approach, applicable to problems which are large, non-deterministic, non linear and discrete in nature. Crossover is the primary operator distinguishing Genetic algorithms from other stochastic search methods, but its role in GAs needs to be better understood. Mutation plays important role in Genetic algorithm to preserving and introducing diversity. In this paper, an evolutionary scheme for adapting the crossover and mutation probabilities is proposed. Experimental results show that the proposed scheme significantly improves the performance of genetic algorithms and outperforms previous work.