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Exponential Slowdown for Larger Populations: The $(μ+1)$-EA on
  Monotone Functions
v1v2v3 (latest)

Exponential Slowdown for Larger Populations: The (μ+1)(μ+1)(μ+1)-EA on Monotone Functions

Foundations of Genetic Algorithms (FOGA), 2019
30 July 2019
Johannes Lengler
Xun Zou
ArXiv (abs)PDFHTML

Papers citing "Exponential Slowdown for Larger Populations: The $(μ+1)$-EA on Monotone Functions"

13 / 13 papers shown
Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler
Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler
Diederick Vermetten
Johannes Lengler
Dimitri Rusin
Thomas Bäck
Carola Doerr
189
3
0
24 Apr 2024
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization
Chao Qian
Ke Xue
Ren-Jian Wang
383
18
0
19 Jan 2024
Hardest Monotone Functions for Evolutionary Algorithms
Hardest Monotone Functions for Evolutionary AlgorithmsEvoStar Conferences (EvoStar), 2023
Marc Kaufmann
Maxime Larcher
Johannes Lengler
Oliver Sieberling
244
3
0
13 Nov 2023
Runtime Analysis of Quality Diversity Algorithms
Runtime Analysis of Quality Diversity AlgorithmsAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2023
Jakob Bossek
Dirk Sudholt
284
6
0
30 May 2023
Analysing Equilibrium States for Population Diversity
Analysing Equilibrium States for Population DiversityAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2023
Johannes Lengler
Andre Opris
Dirk Sudholt
114
6
0
19 Apr 2023
OneMax is not the Easiest Function for Fitness Improvements
OneMax is not the Easiest Function for Fitness Improvements
Marc Kaufmann
Maxime Larcher
Johannes Lengler
Xun Zou
LRM
233
9
0
14 Apr 2022
Self-adjusting Population Sizes for the $(1, λ)$-EA on Monotone
  Functions
Self-adjusting Population Sizes for the (1,λ)(1, λ)(1,λ)-EA on Monotone FunctionsParallel Problem Solving from Nature (PPSN), 2022
Marc Kaufmann
Maxime Larcher
Johannes Lengler
Xun Zou
307
10
0
01 Apr 2022
Two-Dimensional Drift Analysis: Optimizing Two Functions Simultaneously
  Can Be Hard
Two-Dimensional Drift Analysis: Optimizing Two Functions Simultaneously Can Be HardParallel Problem Solving from Nature (PPSN), 2022
D. Janett
Johannes Lengler
275
2
0
28 Mar 2022
Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms:
  Why Success Rates Matter
Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates MatterAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2021
Mario Alejandro Hevia Fajardo
Dirk Sudholt
299
23
0
12 Apr 2021
Runtime analysis of the (mu+1)-EA on the Dynamic BinVal function
Runtime analysis of the (mu+1)-EA on the Dynamic BinVal function
Johannes Lengler
Simone Riedi
144
0
0
26 Oct 2020
Large Population Sizes and Crossover Help in Dynamic Environments
Large Population Sizes and Crossover Help in Dynamic Environments
Johannes Lengler
Jonas Meier
121
23
0
21 Apr 2020
Fast Mutation in Crossover-based Algorithms
Fast Mutation in Crossover-based Algorithms
Denis Antipov
M. Buzdalov
Benjamin Doerr
284
37
0
14 Apr 2020
Exponential Upper Bounds for the Runtime of Randomized Search Heuristics
Exponential Upper Bounds for the Runtime of Randomized Search HeuristicsParallel Problem Solving from Nature (PPSN), 2020
Benjamin Doerr
366
11
0
13 Apr 2020
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