Evolutionary Operator Self-adaptation with Diverse Operators
Operator adaptation in evolutionary computation has previously been applied to either small numbers of operators, or larger numbers of fairly similar ones. This paper focuses on adaptation in algorithms offering a diverse range of operators. We compare a number of previously-developed adaptation strategies, together with two that have been specifically designed for this situation. Probability Matching and Adaptive Pursuit methods performed reasonably well in this scenario, but a strategy combining aspects of both performed better. Multi-Arm Bandit techniques performed well when parameter settings were suitably tailored to the problem, but this tailoring was difficult, and performance was very brittle when the parameter settings were varied.
KeywordsAdaptive operator selection Adaptive pursuit Probability matching Multi-armed bandit Evolutionary algorithm
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- 5.Goldberg, D.: Probability matching, the magnitude of reinforcement, and classifier system bidding. Machine Learning 5(4), 407–425 (1990)Google Scholar
- 6.Hoai, N.: A Flexible Representation for Genetic Programming: Lessons from Natural Language Processing. Ph.D. thesis, University of New South Wales, Australian Defence Force Academy (2004)Google Scholar
- 8.Kim, D., McKay, R.I., Haisoo, S., Yun-Geun, L., Xuan, N.X.: Ecological application of evolutionary computation: Improving water quality forecasts for the nakdong river, korea. In: World Congress on Computational Intelligence, pp. 2005–2012. IEEE Press (2010)Google Scholar
- 10.Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)Google Scholar
- 11.Koza, J.R.: Genetic Programming II Automatic Discovery of Reusable Programs. MIT Press (1994)Google Scholar
- 17.Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 1539–1546. ACM, New York (2005)Google Scholar