New adaptive genetic algorithm based on ranking

In the adaptive genetic algorithm (AGA), the population converges easily to the locally optimal individuals, because the probabilities of crossover and mutation are determined by fitness of solutions. This paper proposes an improved adaptive genetic algorithm based on ranking. The conception of disruptive selection is firstly brought into selection operator. The selection probability based on the ranking value of individual guarantees the maintaining of diversity in population and reservation of elitist. To improve the search capacity, the probabilities of crossover and mutation are also adaptively varied depending on the ranking value of individuals instead of fitness value. Experimental results show that the improved adaptive genetic algorithm can sustain diversity in the population efficiently and find the optimal individual quickly.