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Found 6 papers, 1 papers with code
Fast Re-Optimization of LeadingOnes with Frequent Changes
In this work, we first show that the re-optimization approach suggested by Doerr et al. reaches a limit when the problem instances are prone to more frequent changes.
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 running times that can be achieved by $(1+\lambda)$-type search heuristics on common benchmark problems, we recently suggested a dynamic programming approach that computes optimal expected running times and the regret values inferred when deviating from the optimal parameter choice.
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm
We demonstrate that our HQL mechanism achieves equal or superior performance to all techniques tested in [Rodionova et al., GECCO'19] and this -- in contrast to previous parameter control methods -- simultaneously for all offspring population sizes $\lambda$.
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates
We observe that for the 2-rate EA and the EA with multiplicative update rules the more generous bound $p_{\min}=1/n^2$ gives better results than $p_{\min}=1/n$ when $\lambda$ is small.
Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution
In addition, we present theoretical analysis of the proposed modification on the XdivK problem.
Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range
Online parameter controllers for evolutionary algorithms adjust values of parameters during the run of an evolutionary algorithm.
