Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms:
Why Success Rates Matter

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Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates Matter

12 April 2021

Mario Alejandro Hevia Fajardo

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Papers citing "Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates Matter"

7 / 7 papers shown

Title
EvoPress: Accurate Dynamic Model Compression via Evolutionary Search Oliver Sieberling Denis Kuznedelev Eldar Kurtic Dan Alistarh MQ 71 5 0 18 Oct 2024
Hardest Monotone Functions for Evolutionary Algorithms Marc Kaufmann Maxime Larcher Johannes Lengler Oliver Sieberling 64 2 0 13 Nov 2023
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima J. Jorritsma Johannes Lengler Dirk Sudholt 58 15 0 19 Apr 2023
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
Two-Dimensional Drift Analysis: Optimizing Two Functions Simultaneously Can Be Hard D. Janett Johannes Lengler 54 3 0 28 Mar 2022
Choosing the Right Algorithm With Hints From Complexity Theory Shouda Wang Weijie Zheng Benjamin Doerr 119 17 0 14 Sep 2021