8 citations · 8 across the 2 of their papers we have counts for
2 papers
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…
cs.NE2019★ 8 cited
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates
Anna Rodionova, Kirill Antonov, Arina Buzdalova +1
We analyze the performance of the 2-rate Evolutionary Algorithm (EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a ~EA variant using multi…