Sequential parameter optimization

Abstract

Sequential parameter optimization is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. To demonstrate its flexibility, three scenarios are discussed: (1) no experience how to choose the parameter setting of an algorithm is available, (2) a comparison with other algorithms is needed, and (3) an optimization algorithm has to be applied effectively and efficiently to a complex real-world optimization problem. Although sequential parameter optimization relies on enhanced statistical techniques such as design and analysis of computer experiments, it can be performed algorithmically and requires basically the specification of the relevant algorithm’s parameters.

DOI: 10.1109/CEC.2005.1554761

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@inproceedings{BartzBeielstein2005SequentialPO, title={Sequential parameter optimization}, author={Thomas Bartz-Beielstein and Christian Lasarczyk and Mike Preuss}, booktitle={Congress on Evolutionary Computation}, year={2005} }