Efficient Real-Coded Genetic Algorithms with Flexible-Step Crossover
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Real-coded genetic algorithms (GAs) are effective methods for function optimization. Generally speaking, the major crossover methods used in real-coded GAs require a large execution time for calculating the fitness of many children at each crossover. Thus, a new crossover method is needed for searching such a large search space efficiently. A novel crossover method that generates children stepwise is proposed and applied to the conventional generation-alternation model. In experiments based on standard test functions and actual problems, the proposed model found an optimal solution 30-40% faster than did the conventional model.
- IEEJ Transactions on Electronics, Information and Systems
IEEJ Transactions on Electronics, Information and Systems 126(5), 654-660, 2006-05-01
The Institute of Electrical Engineers of Japan