On the impact of the performance metric on efficient algorithm configuration

DOI10.1016/j.artint.2021.103629OpenAlexW3212226165MaRDI QIDQ2667810

Dirk Sudholt, Pietro S. Oliveto, George T. Hall

Publication date: 2 March 2022

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

Full work available at URL: https://doi.org/10.1016/j.artint.2021.103629

zbMATH Keywords

runtime analysisparameter tuningperformance metricscutoff timealgorithm configurators

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Related Items (1)

When move acceptance selection hyper-heuristics outperform metropolis and elitist evolutionary algorithms and when not

Uses Software

• Paramils
• SPOT
• irace
• SATenstein
• SMAC

Cites Work

• Simplified drift analysis for proving lower bounds in evolutionary computation
• On the analysis of the \((1+1)\) evolutionary algorithm
• Black-box search by unbiased variation
• Optimal parameter choices via precise black-box analysis
• On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms
• The choice of the offspring population size in the \((1,\lambda)\) evolutionary algorithm
• Performance analysis of randomised search heuristics operating with a fixed budget
• SATenstein: automatically building local search SAT solvers from components
• Black-box Complexity of Parallel Search with Distributed Populations
• Fixed Budget Performance of the (1+1) EA on Linear Functions
• The Sequential Parameter Optimization Toolbox
• ParamILS: An Automatic Algorithm Configuration Framework
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