On the Effectiveness of Simple Success-Based Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems

4 March 2018

Papers citing "On the Effectiveness of Simple Success-Based Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems"

7 / 7 papers shown

Title
Benchmarking in Optimization: Best Practice and Open Issues Thomas Bartz-Beielstein Carola Doerr Daan van den Berg Jakob Bossek Sowmya Chandrasekaran ... B. Naujoks Patryk Orzechowski Vanessa Volz Markus Wagner T. Weise 138 112 0 07 Jul 2020
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm Arina Buzdalova Carola Doerr A. Rodionova 25 3 0 19 Jun 2020
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem Luca Mossina Emmanuel Rachelson D. Delahaye 13 0 0 09 May 2019
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates A. Rodionova Kirill Antonov Arina Buzdalova Carola Doerr 44 13 0 17 Apr 2019
Maximizing Drift is Not Optimal for Solving OneMax Nathan Buskulic Carola Doerr 53 23 0 16 Apr 2019
Self-Adjusting Mutation Rates with Provably Optimal Success Rules Benjamin Doerr Carola Doerr Johannes Lengler 161 50 0 07 Feb 2019
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation Furong Ye Carola Doerr Thomas Bäck 66 24 0 17 Jan 2019