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A novel sexual adaptive genetic algorithm based on two-step evolutionary scenario of baldwin effect and analysis of global convergence
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ACM/SIGEVO Summit on Genetic and Evolutionary Computation archive
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation table of contents
Shanghai, China
SESSION: Full papers table of contents
Pages 737-744  
Year of Publication: 2009
ISBN:978-1-60558-326-6
Authors
Mingming Zhang  Donghua University, Shanghai, China
Shuguang Zhao  Donghua University, Shanghai, China
Xu Wang  Donghua University, Shanghai, China
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

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.


REFERENCES

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Collaborative Colleagues:
Mingming Zhang: colleagues
Shuguang Zhao: colleagues
Xu Wang: colleagues