Dynamic Parameter Changing During the Run to Speed Up Evolutionary Algorithms: PPSN 2020 tutorial
Authors
G. Papa
Publication
Parallel problem solving from nature PPSN 2020
Leiden, The Netherlands, 5-9 September, 2020
Abstract
One of the most challenging problems in solving optimization problems with evolutionary algorithms is the selection of several control parameters for adjusting the behaviour of the algorithms. Suitable control parameter values need to be found, and their choice can have a significant impact on the performance of the algorithm. Different parameter settings can be optimal at different stages of the optimization process: at the beginning, in the exploration phase of an optimization process, one may want to increase the chance of finding the most promising regions of the search space, while later in the exploitation phase, to stay focused within the promising area. The ambition of this tutorial is to contribute towards a more systematic use of dynamic parameter choices/setting. We survey existing techniques to automatically select control parameter values on the fly. We will discuss both theoretical and experimental results that demonstrate the unexploited potential of non-static parameter choices.
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