Grid-Based Pseudo-Parallel Genetic Algorithm and Its Application |
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| Authors: | CHEN Hai-ying GUO Qiao XU Li |
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| Affiliation: | School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China;School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China;School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | Aimed at the problems of premature and lower convergence of simple genetic algorithms (SGA), three ideas--partition the whole search uniformly, multi-genetic operators and multi-populations evolving independently are introduced, and a grid-based pseudo-parallel genetic algorithms (GPPGA) is put forward. Thereafter, the analysis of premature and convergence of GPPGA is made. In the end, GPPGA is tested by both six-peak camel back function, Rosenbrock function and BP network. The result shows the feasibility and effectiveness of GPPGA in overcoming premature and improving convergence speed and accuracy. |
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| Keywords: | genetic algorithms parallel grid neural network weights optimizing |
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