Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Global Optimization by Multilevel Coordinate Search
Journal of Global Optimization
Foundations of Global Genetic Optimization
Foundations of Global Genetic Optimization
BBOB-benchmarking the DIRECT global optimization algorithm
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking the BFGS algorithm on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking the NEWUOA on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space DIRECT, MCS possibly combined with a local search method MCS, or a multi-start approach NEWUOA, GLOBAL possibly equipped with a careful selection of points to run a local optimizer from GLOBAL. The recently proposed "comparing continuous optimizers" COCO methodology was adopted as the basis for the comparison. Based on the results, we draw suggestions about which algorithm should be used depending on the available budget of function evaluations, and we propose several possibilities for hybridizing evolutionary algorithms EAs with features of the other compared algorithms.