Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Computers and Operations Research
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Convergence of the Nelder--Mead Simplex Method to a Nonstationary Point
SIAM Journal on Optimization
Global Optimization by Multilevel Coordinate Search
Journal of Global Optimization
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
BBOB-benchmarking two variants of the line-search algorithm
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
BBOB-benchmarking the Rosenbrock's local search algorithm
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking the nelder-mead downhill simplex algorithm with many local restarts
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
Population-based algorithm portfolios for numerical optimization
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
VXQR: derivative-free unconstrained optimization based on QR factorizations
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Bi-population CMA-ES agorithms with surrogate models and line searches
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Several local search algorithms for real-valued domains axis parallel line search, Nelder-Mead simplex search, Rosenbrock's algorithm, quasi-Newton method, NEWUOA, and VXQR are described and thoroughly compared in this article, embedding them in a multi-start method. Their comparison aims 1 to help the researchers from the evolutionary community to choose the right opponent for their algorithm to choose an opponent that would constitute a hard-to-beat baseline algorithm, 2 to describe individual features of these algorithms and show how they influence the algorithm on different problems, and 3 to provide inspiration for the hybridization of evolutionary algorithms with these local optimizers. The recently proposed Comparing Continuous Optimizers COCO methodology was adopted as the basis for the comparison. The results show that in low dimensional spaces, the old method of Nelder and Mead is still the most successful among those compared, while in spaces of higher dimensions, it is better to choose an algorithm based on quadratic modeling, such as NEWUOA or a quasi-Newton method.