Self-adjusting offspring population sizes outperform fixed parameters on the Cliff function

DOI10.1016/J.ARTINT.2023.104061MaRDI QIDQ6494347

Dirk Sudholt, Mario Alejandro Hevia Fajardo

Publication date: 30 April 2024

Published in: Artificial Intelligence (Search for Journal in Brave)

zbMATH Keywords

drift analysisnon-elitismruntime analysisparameter controlmultimodal optimisation

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Cites Work

• From black-box complexity to designing new genetic algorithms
• Simplified drift analysis for proving lower bounds in evolutionary computation
• The \((1+\lambda)\) evolutionary algorithm with self-adjusting mutation rate
• Optimal static and self-adjusting parameter choices for the \((1+(\lambda ,\lambda ))\) genetic algorithm
• Static and self-adjusting mutation strengths for multi-valued decision variables
• A study of drift analysis for estimating computation time of evolutionary algorithms
• Black-box search by unbiased variation
• Self-adjusting evolutionary algorithms for multimodal optimization
• When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms
• Self-adjusting mutation rates with provably optimal success rules
• The choice of the offspring population size in the \((1,\lambda)\) evolutionary algorithm
• First-hitting times under drift
• Towards a runtime comparison of natural and artificial evolution
• Runtime analysis for self-adaptive mutation rates
• Do additional target points speed up evolutionary algorithms?
• Black-box Complexity of Parallel Search with Distributed Populations
• Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions
• Self-adjusting offspring population sizes outperform fixed parameters on the cliff function
• Generalized drift analysis in continuous domain
• Theory of Evolutionary Computation
• Adaptive population models for offspring populations and parallel evolutionary algorithms

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