a novel sexual adaptive genetic algorithm based on two-step evolutionary scenario of baldwin effect and analysis of global convergence
This work presents a novel sexual adaptive genetic algorithm (NSAGA) based on two-step evolutionary scenario of Baldwin effect to overcome the shortcomings of traditional genetic algorithms, such as premature convergence, stochastic roaming, and poor capabilities in local exploring. NSAGA simulates sexual reproduction in nature and utilizes an effective gender determination method to divide the evolutionary population into two different gender subgroups. Based on the competition, cooperation, and innate differences between two gender subgroups, NSAGA adaptively adjusts the sexual genetic operators. To guide the individuals' evolution, NSAGA adopts a two-step evolutionary scenario: NSAGA guides individuals in niche to forward or reverse evolutionary learning inspired by the acquired reinforcement learning theory based on Baldwin effect, and enables the transmission of fitness information between parents and offspring to supervise the offspring's evolution. Then, the global convergence analysis of NSAGA is presented in detail. It is theoretically proved that NSAGA can converge to the global optimum and the epsilon-optimal solution with probability one. Moreover, numerical simulations are conducted for a set of benchmark test functions, and the performance of NSAGA is compared with that of some evolutionary algorithms published recently. Experiments results show that the proposed algorithm is effective and advantageous.
adaptation genetic algorithm baldwin effect global convergence sexual reproduction niche