Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Global optimization and simulated annealing
Mathematical Programming: Series A and B
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The bisection method in higher dimensions
Mathematical Programming: Series A and B
A deterministic algorithm for global optimization
Mathematical Programming: Series A and B
Complexity of Bezout's theorem III: condition number and packing
Journal of Complexity - Festschrift for Joseph F. Traub, Part 1
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Function approximation on non-Euclidean spaces
Neural Networks
Simulated annealing algorithm with biased neighborhood distribution for training profile models
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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This article presents a new algorithm, called the’’Hyperbell Algorithm‘‘, that searches for the global extrema ofnumerical functions of numerical variables. The algorithm relies on theprinciple of a monotone improving random walk whose steps aregenerated around the current position according to a gradually scaleddown Cauchy distribution. The convergence of the algorithm is provenand its rate of convergence is discussed. Its performance is tested onsome ’’hard‘‘ test functions and compared to that of other recentalgorithms and possible variants. An experimental study of complexityis also provided, and simple tuning procedures for applications areproposed.