Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models
A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Multimodal optimization by means of a topological species conservation algorithm
IEEE Transactions on Evolutionary Computation
When parameter tuning actually is parameter control
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Resampling methods for meta-model validation with recommendations for evolutionary computation
Evolutionary Computation
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In the current study, parameter tuning is performed for two evolutionary optimization techniques, Covariance Matrix Adaptation Evolution Strategy and Topological Species Conservation. They are applied for three multimodal benchmark functions with various properties and several outputs are considered. A data set with input parameters and metaheuristic outcomes is used for training four surrogate models. They are then each used by a genetic algorithm that is employed for searching the best parameter settings for the initial approaches. The genetic algorithm uses the model outputs as the direct fitness evaluation and only the best found parameter setting is tested within the original metaheuristics. Each model quality is priory evaluated, but they are all subsequently used in the search process to observe how the (in)accuracy influences the final result. Additionally, the genetic algorithm is used for tuning these approaches directly to test if the search conducts to the same parameter set, or at least close to it.