Publication | Closed Access
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
430
Citations
20
References
2010
Year
Unknown Venue
Large-scale Global OptimizationEngineeringMachine LearningBlack-box OptimizationRuntime DistributionAlgorithm ConfigurationComputational ComplexityEmpirical AlgorithmicsEvolutionary Multimodal OptimizationBbob-2009 BenchmarkingDerivative-free OptimizationParallel ComputingApproximation TheoryIntelligent OptimizationComputer EngineeringLarge Scale OptimizationComputer ScienceSignal ProcessingModel OptimizationLocal Search (Optimization)Continuous Domain
This paper presents results of the BBOB-2009 benchmarking of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. The runtime of the algorithms, measured in number of function evaluations, is investigated and a connection between a single convergence graph and the runtime distribution is uncovered. Performance is investigated for different dimensions up to 40-D, for different target precision values, and in different subgroups of functions. Searching in larger dimension and multi-modal functions appears to be more difficult. The choice of the best algorithm also depends remarkably on the available budget of function evaluations.
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