An algorithm for finding the global maximum of a multimodal, multivariate function
Mathematical Programming: Series A and B
New computer methods for global optimization
New computer methods for global optimization
Global optimization
Implementing Valiant's Learnability Theory Using Random Sets
Machine Learning
The bisection method in higher dimensions
Mathematical Programming: Series A and B
Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) for fast global optimization
Journal of Optimization Theory and Applications
The annealing evolution algorithm as function optimizer
Parallel Computing
Global optimization requires global information
Journal of Optimization Theory and Applications
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
A filled function method for finding a global minimizer on global integer optimization
Journal of Computational and Applied Mathematics
A hybrid meta-heuristic for global optimisation using low-discrepancy sequences of points
Computers and Operations Research
A filled function method for finding a global minimizer on global integer optimization
Journal of Computational and Applied Mathematics
A local search method for continuous global optimization
Journal of Global Optimization
Global optimization using evolutionary algorithm based on level set evolution and latin square
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Global optimization algorithms using fourier smoothing
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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A stochastic approach to solving unconstrained continuous-function global optimization problems is presented. It builds on the tunneling approach to deterministic optimization presented by Barhen and co-workers (Bahren and Protopopescu, in: State of the Art in Global Optimization, Kluwer, 1996; Barhen et al., Floudas and Pardalos (eds.), TRUST: a deterministic algorithm for global optimization, 1997) by combining a series of local descents with stochastic searches. The method uses a rejection-based stochastic procedure to locate new local minima descent regions and a fixed Lipschitz-like constant to reject unpromising regions in the search space, thereby increasing the efficiency of the tunneling process. The algorithm is easily implemented in low-dimensional problems and scales easily to large problems. It is less effective without further heuristics in these latter cases, however. Several improvements to the basic algorithm which make use of approximate estimates of the algorithms parameters for implementation in high-dimensional problems are also discussed. Benchmark results are presented, which show that the algorithm is competitive with the best previously reported global optimization techniques. A successful application of the approach to a large-scale seismology problem of substantial computational complexity using a low-dimensional approximation scheme is also reported.