Abstract
AbstractParameter control of evolutionary algorithm (EA) poses special challenges as EA uses a population. A widely practiced approach to identify a good set of parameters for a particular class of problem is through experimentations. Ideally, the parameter selection should depend on the resource availability, and thus, a rigid choice may not be suitable. In this paper, we attempt to address the problem of parameter control of EA under given time constraints. We propose an automated framework for parameter selection, which can adapt according to the constraints specified. We propose both static and dynamic selection strategies based on a probabilistic profiling method. Experiments performed with traveling salesman problem (TSP) show that an adaptive parameter control mechanism can yield better results than a static selection.KeywordsEvolutionary AlgorithmTravel Salesman ProblemTravel Salesman ProblemWeighted Average MethodPopulation QualityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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