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20162022
most citedOffspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates

8 citations · 9 across the 6 of their papers we have counts for

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cs.NE2020

Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm

Arina Buzdalova, Carola Doerr, Anna Rodionova

It is well known that evolutionary algorithms (EAs) achieve peak performance only when their parameters are suitably tuned to the given problem. Even more, it is known that the bes…