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
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Global Optimization on Funneling Landscapes
Journal of Global Optimization
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Global Minimization via Piecewise-Linear Underestimation
Journal of Global Optimization
A Population-based Approach for Hard Global Optimization Problems based on Dissimilarity Measures
Mathematical Programming: Series A and B
Search space pruning and global optimisation of multiple gravity assist spacecraft trajectories
Journal of Global Optimization
A particle swarm pattern search method for bound constrained global optimization
Journal of Global Optimization
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A global optimization method for the design of space trajectories
Computational Optimization and Applications
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Clonal selection: an immunological algorithm for global optimization over continuous spaces
Journal of Global Optimization
Function optimisation by learning automata
Information Sciences: an International Journal
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In this paper we introduce the LeGO (Learning for Global Optimization) approach for global optimization in which machine learning is used to predict the outcome of a computationally expensive global optimization run, based upon a suitable training performed by standard runs of the same global optimization method. We propose to use a Support Vector Machine (although different machine learning tools might be employed) to learn the relationship between the starting point of an algorithm and the final outcome (which is usually related to the function value at the point returned by the procedure). Numerical experiments performed both on classical test functions and on difficult space trajectory planning problems show that the proposed approach can be very effective in identifying good starting points for global optimization.