Evolutionary algorithm with modifications in the reproduction phase

All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The number of calculations in these algorithms is generally proportional to population size. Classical genetic algorithms (GAs), for instance, require the calculation of the fitness values of every individual in the population. A new evolutionary algorithm that combines two basic operators of GAs: namely, selection and crossover are proposed. The new operator is applied to two randomly selected individuals from the existing population. It is shown that the number of calculations is reduced greatly compared to classical GAs, while performance is enhanced on the functions studied. Moreover, simulation studies indicate that the new algorithm can produce a performance comparable with he more intelligent and hybrid evolutionary techniques given in the related literature.