Rank based crossover-a new technique to improve the speed and quality of convergence in GA

A new technique called rank based crossover (RBC) to improve the speed of reaching optimal solutions is introduced for genetic algorithms (GAs). In real life, marriages (crossovers) occur between two individuals of similar status in society and/or from neighboring localities. This principle is extended in case of GAs while selecting crossover partners. In the proposed strategy, the probability of crossover is more when their rank in the whole population is close. This probability function changes with advancing generations, so that the effect of RBC is negligible in the beginning and gradually increases. It could easily control fine tuning of the good chromosomes to achieve fast convergence and reach optimum values. Also, the scheme is not centralized like the elitist approach. Different schemes of the probability function are tried and evaluated. The effectiveness of this new method has been demonstrated on the problems of maximizing complex multi-modal functions. The results are compared with standard genetic algorithms (SGA). Another technique called “fitness scaling” is widely used to adaptively scale the objective function to achieve similar goal. We also compared our results with the “linear fitness scaling” strategy. Results using our RBC strategy are found to be superior to those of the fitness scaling method and the SGA in terms of probability of hitting the maximum value as well as speed of finding the maximum