Integrated vs. sequential approaches for selecting and tuning CMA-ES variants
Genetic and Evolutionary Computation Conference (GECCO), pp. 903–912
Abstract
When faced with a specific optimization problem, deciding which algorithm to apply is always a difficult task. Not only is there a vast variety of algorithms to select from, but these algorithms are often controlled by many hyperparameters, which need to be suitably tuned in order to achieve peak performance. Usually, the problem of selecting and configuring the optimization algorithm is addressed sequentially, by first selecting a suitable algorithm and then tuning it for the application at hand. Integrated approaches, commonly known as Combined Algorithm Selection and Hyperparameter (CASH) solvers, have shown promise in several applications.
Authors 4
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Affiliation as printed
Leiden Institute for Advanced Computer Science, Leiden, The Netherlands
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Affiliation as printed
Sorbonne Université, Paris, France
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Affiliation as printed
Sorbonne Université, Paris, France
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Affiliation as printed
Leiden Institute for Advanced Computer Science, Leiden, The Netherlands
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