A hybrid self-adaptive genetic algorithm based on sexual reproduction and Baldwin effect for global optimization

Authors:
Mingming Zhang;Shuguang Zhao;Xu Wang
Affiliations:
College of Information Science and Technology, Donghua University, Shanghai, China and Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Donghua Unive ...;College of Information Science and Technology, Donghua University, Shanghai, China and Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Donghua Unive ...;College of Information Science and Technology, Donghua University, Shanghai, China and Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Donghua Unive ...
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

Global optimization problems with numerous local and global optima are difficult to solve, which can trap traditional genetic algorithms. To solve the problems, a hybrid self-adaptive genetic algorithm based on sexual reproduction and Baldwin effect is presented for global optimization in this paper. By simulating sexual reproduction in nature, the proposed algorithm utilizes a gender determination method to determine the gender of individuals in population. Then, it adopts the different initial genetic parameters for female and male subgroups, and self-adaptively adjusts the sexual genetic operation based on the competition and cooperation between different gender subgroups. Furthermore, the fitness information transmission between parents and offspring is implemented to guide the evolution of individuals' acquired fitness. Moreover, on the basis of the Darwinian evolution theory, the proposed algorithm guides individuals to forward or reverse acquired reinforcement learning based on Baldwin effect in niche. Numerical simulations are conducted for a set of benchmark functions with different dimensional decision variables. The results show that the proposed algorithm can find optimal or closer-to-optimal solution, and has faster search speed and higher convergence rate.