Random tunneling by means of acceptance-rejection sampling for global optimization
Journal of Optimization Theory and Applications
Optimization
Recent advances in global optimization
Recent advances in global optimization
A deterministic algorithm for global optimization
Mathematical Programming: Series A and B
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
New Classes of Globally Convexized Filled Functions for Global Optimization
Journal of Global Optimization
A recursive random search algorithm for large-scale network parameter configuration
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Cutting Angle Method and a Local Search
Journal of Global Optimization
A recursive random search algorithm for network parameter optimization
ACM SIGMETRICS Performance Evaluation Review
Computers & Mathematics with Applications
Using Global Optimization to Explore Multiple Solutions of Clustering Problems
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Interactive Visualization Tools for Meta-Clustering
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Metaclustering and Consensus Algorithms for Interactive Data Analysis and Validation
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Multiple data structure discovery through global optimisation, meta clustering and consensus methods
International Journal of Knowledge Engineering and Soft Data Paradigms
Global optimization, meta clustering and consensus clustering for class prediction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Multiple clustering solutions analysis through least-squares consensus algorithms
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
CARTopt: a random search method for nonsmooth unconstrained optimization
Computational Optimization and Applications
| Hi-index | 0.01 |
We present an algorithm for finding a global minimum of a multimodal,multivariate function whose evaluation is very expensive, affected by noise andwhose derivatives are not available. The proposed algorithm is a new version ofthe well known Price‘s algorithm and its distinguishing feature is that ittries to employ as much as possible the information about the objectivefunction obtained at previous iterates. The algorithm has been tested on alarge set of standard test problems and it has shown a satisfactorycomputational behaviour. The proposed algorithm has been used to solveefficiently some difficult optimization problems deriving from the study ofeclipsing binary star light curves.