research
∙
09/09/2022
Fast Re-Optimization of LeadingOnes with Frequent Changes
In real-world optimization scenarios, the problem instance that we are a...
share
research
∙
02/23/2021
Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms
With the goal to provide absolute lower bounds for the best possible run...
research
∙
06/19/2020
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm
It is well known that evolutionary algorithms (EAs) achieve peak perform...
research
∙
04/17/2019
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates
We analyze the performance of the 2-rate (1+λ) Evolutionary Algorithm (E...
research
∙
04/24/2017
Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution
Efficiency of single-objective optimization can be improved by introduci...
research
∙
03/22/2016
