Abstract
In this paper, the new methods of optimizing Genetic Algorithm control parameters are presented, including the method of adjusting crossover probability and mutation probability, the dynamic convergence rule and the method of determining the optimal population size. All the methods can be applied to enhancing Genetic Algorithm running efficiency and preventing premature convergence.
| Original language | English |
|---|---|
| Pages | 2504-2507 |
| Number of pages | 4 |
| State | Published - 2002 |
| Event | Proceedings of the 4th World Congress on Intelligent Control and Automation - Shanghai, China Duration: 10 Jun 2002 → 14 Jun 2002 |
Conference
| Conference | Proceedings of the 4th World Congress on Intelligent Control and Automation |
|---|---|
| Country/Territory | China |
| City | Shanghai |
| Period | 10/06/02 → 14/06/02 |
Keywords
- Control parameters
- Crossover probability
- Genetic algorithm
- Mutation probability
- Population size