Operator Self-adaptation in Genetic Programming
We investigate the application of adaptive operator selection rates to Genetic Programming. Results confirm those from other areas of evolutionary algorithms: adaptive rate selection out-performs non-adaptive methods, and among adaptive methods, adaptive pursuit out-performs probability matching. Adaptive pursuit combined with a reward policy that rewards the overall fitness change in the elite worked best of the strategies tested, though not uniformly on all problems.
KeywordsGenetic Programming Adaptive Operator Selection Adaptive Pursuit Probability Matching Evolutionary Algorithm Tree Adjoining Grammar Grammar Guided Genetic Programming
Unable to display preview. Download preview PDF.
- 3.Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: Proceedings of the 7th Genetic and Evolutionary Computation Conference, pp. 1539–1546 (2005)Google Scholar
- 6.Thierens, D.: Adaptive strategies for operator allocation. Parameter Setting in Evolutionary Algorithms, 77–90 (2007)Google Scholar
- 7.Goldberg, D.: Probability matching, the magnitude of reinforcement, and classifier system bidding. Machine Learning 5(4), 407–425 (1990)Google Scholar
- 14.Murphy, E., O’Neill, M., Galván-López, E., Brabazon, A.: Tree-adjunct grammatical evolution. In: 2010 IEEE Congress on Evolutionary Computation (CEC), July 1-8 (2010)Google Scholar