Real‐world Applications of Dynamic Parameter Control in Evolutionary Computation - tutorial
G. Papa
Parallel problem solving from nature PPSN 2022
Dortmund, Germany, 10-14 September 2022, 2022
While solving optimization problems with evolutionary algorithms one basic challenge is the selection of the proper control parameters, which adjust the behaviour of the algorithms. Several control parameters can be set in searching for the optimum of an objective function. Suitable control parameter values need to be found for the population size, the mutation strength, the crossover rate, the selective pressure, etc. The choice of these parameters can have a significant impact on the performance of the algorithm and thus need to be executed with care. With parameter control approach no prior training of parameters is needed. It also accounts for the fact that the optimal parameter values typically change during the optimization process: for example, at the beginning of an optimization process we typically aim for exploration, while in the later stages we want the algorithm to converge and to focus its search on the most promising regions in the search space. The ambition of this tutorial is to inform participants about different parameter control techniques, and by discussing both theoretical and experimental results that demonstrate the unexploited potential of non‐static parameter choices. <a href="" target="_new"></a>
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