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.

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