Simple on-the-fly parameter selection mechanisms for two classical discrete black-box optimization benchmark problems
Authors:
Open Access
- 2 July 2018
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 943-950
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- Australian Research Council (DE160100850)
- Agence Nationale de la Recherche (ANR-11-LABX-0056-LMH)
This publication has 22 references indexed in Scilit:
- Optimal Static and Self-Adjusting Parameter Choices for the $(1+(\lambda ,\lambda ))$ ( 1 + ( λ , λ ) ) Genetic AlgorithmAlgorithmica, 2017
- The (1+ λ ) evolutionary algorithm with self-adjusting mutation rate2017
- Provably Optimal Self-adjusting Step Sizes for Multi-valued Decision Variables2016
- Optimal Parameter Choices via Precise Black-Box Analysis2016
- The (1+1) Elitist Black-Box Complexity of LeadingOnes2016
- Optimal Parameter Choices Through Self-Adjustment2015
- The Impact of Random Initialization on the Runtime of Randomized Search HeuristicsAlgorithmica, 2015
- Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem2010
- Benchmarking the (1+1) evolution strategy with one-fifth success rule on the BBOB-2009 function testbed2009
- Extreme Value Based Adaptive Operator Selection2008