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Indexed by:会议论文

Date of Publication:2008-10-18

Included Journals:EI、CPCI-S、Scopus

Volume:1

Page Number:586-+

Key Words:dynamic; mutation; crossover; genetic algorithm; optimization; convergence

Abstract:Conventional genetic algorithm has. drawbacks such as premature convergence and less stability in actual uses. Use conventional mutation and crossover operators should be used is quite difficult and is usually done by trial and error In this paper a new genetic algorithm, the genetic algorithm based on a dynamic mutation operator and a dynamic crossover operator using. self-selecting crossover method (DMO-DSSCMCO-GA), is introduced. Multimodal function optimization is performed to verify the feasibility and effectiveness. The experiment results show that convergence speed and stability are increased by proposed genetic algorithm, and escaped from premature convergence phenomenon.