A novel Genetic Algorithm with multiple sub-population parallel search mechanism

Genetic Algorithm (GA), based on metaphors from the natural evolutionary process, is a famous random heuristic approach for solving complex optimization problems. However, the traditional GA is always subjected to the low convergence velocity and deceptions of multiple local optima. To overcome such inconvenience, a novel GA is proposed which entitled self-adaptive genetic algorithms (SaGA) in this paper. During the execution of the search process, the whole populations are classified into subgroups by sufficiently analyzed the individuals' state. Each individual in a different subset is assigned to the appropriate attribute (probabilities of crossover and mutation, pc, pm). Self-adaptive update the subgroups and adjust the control parameters, which are considered to be an optimal balance between exploration and exploitation. The empirical values and negative feedback technique are also used in parameters selection, which relieve the burden of specifying the parameters values. The new method is tested on a set of well-known benchmark test functions, and the simulation results suggest that it outperforms to other state-of-the-art techniques referred to in this paper in terms of the quality of the final solutions.