Online Black-Box Algorithm Portfolios for Continuous Optimization

In black-box function optimization, we can choose from a wide variety of heuristic algorithms that are suited to different functions and computation budgets. Given a particular function to be optimized, the problem we consider in this paper is how to select the appropriate algorithm. In general, this problem is studied in the field of algorithm portfolios; we treat the algorithms as black boxes themselves and consider online selection (without learning mapping from problem features to best algorithms a priori and dynamically switching between algorithms during the optimization run).

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