8 citations · 8 across the 4 of their papers we have counts for
5 papers
Fast Re-Optimization of LeadingOnes with Frequent Changes
Nina Bulanova, Arina Buzdalova, Carola Doerr
In real-world optimization scenarios, the problem instance that we are asked to solve may change during the optimization process, e.g., when new information becomes available or wh…
Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms
Kirill Antonov, Maxim Buzdalov, Arina Buzdalova +1
With the goal to provide absolute lower bounds for the best possible running times that can be achieved by -type search heuristics on common benchmark problems, we recently…
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…
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…
Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range
Arkady Rost, Irina Petrova, Arina Buzdalova
Online parameter controllers for evolutionary algorithms adjust values of parameters during the run of an evolutionary algorithm. Recently a new efficient parameter controller base…