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Content Provider | IEEE Xplore Digital Library |
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Author | Mingming Zhang Shuguang Zhao Xu Wang |
Copyright Year | 2009 |
Description | Author affiliation: College of Information Science and Technology, Donghua University, Shanghai 201620, China (Mingming Zhang; Shuguang Zhao; Xu Wang) |
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. |
Starting Page | 3087 |
Ending Page | 3094 |
File Size | 308111 |
Page Count | 8 |
File Format | |
ISBN | 9781424429585 |
DOI | 10.1109/CEC.2009.4983334 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-05-18 |
Publisher Place | Norway |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Genetic algorithms Evolution (biology) Biological system modeling Optimization methods Computational biology Numerical simulation Organisms Earth Learning Information processing |
Content Type | Text |
Resource Type | Article |
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