Multiple crossovers between multiple parents to improve search in evolutionary algorithms

As a new promising crossover method, multiple crossovers per couple (MCPC) deserves special attention in the evolutionary computing field. Allowing multiple crossovers per couple on a selected pair of parents provided an extra benefit in processing time and similar quality of solutions when contrasted against the conventional approach, which applies a single crossover operation per couple. These results were confirmed when optimising classic testing functions and harder (non-linear, non-separable) functions. Despite these benefits, due to a reinforcement of selective pressure, MCPC showed in some cases an undesirable premature convergence effect. An adequate balance between exploitation and exploration can improve search. Extreme exploitation can lead to premature convergence and intense exploration can make the search ineffective. Focussing on this equilibrium problem, a previous proposal combined MCPC with an alternative selection method; fitness proportional couple selection (FPCS) which first creates an intermediate population couples where both individuals were chosen proportional selection. Then a criterion is applied to establish the fitness of a couple and subsequently, couples are selected for crossing-over based on couple fitness. This paper investigates the raw effect in performance on a pair of selected optimization problems by using a new multiple crossovers on multiple parents (MCMP) method, which allows multiple recombination of multiple parents under uniform scanning crossover