Cooperative model for genetic operators to improve GAs

This work proposes a new empirical model that puts genetic operators in a cooperative stand with each other. Two parallel operators produce offspring and fulfil specific roles: self-reproduction with mutation (SRM) as a permanent source of diversity to induce the appearance of beneficial mutations and crossover and mutation (CM) to propagate them in the population. An extinctive selection mechanism subjects CM's and SRM's offspring to compete for survival and to guarantee the preservation of beneficial mutations for the next generation. SRM is implemented with an adaptive mutation schedule, which acts depending on SRM's contribution to the actual population, to keep control of the exploration-exploitation balance. Two adaptive mutation schemes are investigated for SRM, adaptive dynamic segment (ADS) and adaptive dynamic probability (ADP). CM's mutation is used to create the appropriate conditions in which SRM's offspring could be competitive with CM's offspring. Thus the expected cooperation between CM and SRM emerges resulting in higher convergence velocity and higher convergence reliability. The proposed model is tested with the 0/1 multiple knapsack NP-hard combinatorial optimization problem where it impressively outperforms a canonical genetic algorithm as well as two other enhanced GAs.