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
Multilayer feedforward networks are universal approximators
Neural Networks
Global optimization
Trace-Based Methods for Solving Nonlinear Global Optimization and Satisfiability Problems
Journal of Global Optimization
Conditions for Global Optimality 2
Journal of Global Optimization
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Class of Augmented Filled Functions
Computational Optimization and Applications
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This paper addresses the problem of global optimization by means of a monotonic transformation. With an observation on global optimality of functions under such a transformation, we show that a simple and effective algorithm can be derived to search within possible regions containing the global optima. Numerical experiments are performed to compare this algorithm with one that does not incorporate transformed information using several benchmark problems. These results are also compared to best known global search algorithms in the literature. In addition, the algorithm is shown to be useful for several neural network learning problems, which possess much larger parameter spaces.