Efficient Genetic Algorithm for High-Dimensional Function Optimization

Abstract

An Efficient Genetic Algorithm(EGA) proposed in this paper was aiming to high-dimensional function optimization. To generate multiple diverse solutions and to strengthen local search ability, the new subspace crossover and timely mutation operators improved by us will be used in EGA. The combination of the new operators allow the integration of randomization and elite solutions analysis to achieve a balance of stability and diversification to further improve the quality of solutions in the case of high-dimensional functions. Standard GA and PRPDPGA proposed already were compared in simulation. Computational studies of benchmark by testing optimization functions suggest that the proposed algorithm was able to quickly achieve good solutions while avoiding being trapped in premature convergence.