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Global optimization with one-class classification-assisted selection
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DOI：10.1016/j.swevo.2020.100801.png)
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Selection in evolutionary algorithms (EAs) selects promising solutions from a set of candidates. Most selection strategies are fitness-driven, where each solution is selected based on its fitness value. This fitness-based strategy leads to a waste of fitness evaluations, since some unpromising solutions are thrown away without providing valuable search information while still being evaluated. Our intention is to reduce the number of fitness evaluations during the selection procedure. We treat selection as a one-class classification procedure so that the offspring solutions similar to the current population, which has the best solutions up to that point, are more likely to be selected. We use the classifier to predict the categories of newly generated solutions. Only those predicted `promising' solutions can be selected for evaluation purposes. The efficiency of EAs can be greatly improved, since the procedure is based on the decision variables (features) before the fitness evaluations. Based on this consideration, we propose a One-class Classification-assisted Selection (OCAS) strategy for EAs. We apply the OCAS strategy to two EAs and study them on three test suites. Our experimental results reveal that the number of fitness evaluations can be clearly decreased by OCAS.
Keyword:
One-class classification
Selection
Evolutionary algorithms
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