Self-Adjusting Mutation Rates with Provably Optimal Success Rules

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Self-Adjusting Mutation Rates with Provably Optimal Success Rules

7 February 2019

Johannes Lengler

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Papers citing "Self-Adjusting Mutation Rates with Provably Optimal Success Rules"

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Title
EvoPress: Accurate Dynamic Model Compression via Evolutionary Search Oliver Sieberling Denis Kuznedelev Eldar Kurtic Dan Alistarh MQ 71 5 0 18 Oct 2024
OneMax is not the Easiest Function for Fitness Improvements Marc Kaufmann Maxime Larcher Johannes Lengler Xun Zou LRM 93 6 0 14 Apr 2022
Hard Problems are Easier for Success-based Parameter Control Mario Alejandro Hevia Fajardo Dirk Sudholt 53 6 0 12 Apr 2022
Effective Mutation Rate Adaptation through Group Elite Selection Akarsh Kumar B. Liu Risto Miikkulainen Peter Stone 28 10 0 11 Apr 2022
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration André Biedenkapp Nguyen Dang Martin S. Krejca Frank Hutter Carola Doerr 74 8 0 07 Feb 2022
Stagnation Detection with Randomized Local Search A. Rajabi Carsten Witt 89 30 0 28 Jan 2021
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
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization Benjamin Doerr Frank Neumann 67 34 0 30 Jun 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
Self-adaptation in non-Elitist Evolutionary Algorithms on Discrete Problems with Unknown Structure B. Case Per Kristian Lehre 35 31 0 01 Apr 2020