The controlled random search algorithm in optimizing regression models
Computational Statistics & Data Analysis
Journal of Global Optimization
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
A population-based algorithm-generator for real-parameter optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Flexible Evolutionary Agent: cooperation and competition among real-coded evolutionary operators
Soft Computing - A Fusion of Foundations, Methodologies and Applications
On the accuracy of statistical procedures in Microsoft Excel 2003
Computational Statistics & Data Analysis
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Editorial: Special Issue on Statistical Algorithms and Software
Computational Statistics & Data Analysis
Adaptation in differential evolution: A numerical comparison
Applied Soft Computing
Morphological analysis of 3D SPECT images via nilpotent t-norms in diagnosis of Alzheimer's disease
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
Hi-index | 0.03 |
Algorithms for the estimation of nonlinear regression parameters are considered. Adaptive population-based search algorithms are proposed and implemented in deriving reliable estimates at a reasonable time with default setting of their controlling parameters. The algorithms are tested on the NIST collection of data sets containing 27 nonlinear regression tasks of various level of difficulty. The experimental results show that both algorithms with competing heuristics are significantly more reliable as compared with the algorithm based on Levenberg-Marquardt optimizing procedure.