Machine learning for global optimization

  title={Machine learning for global optimization},
  author={Andrea Cassioli and David Di Lorenzo and Marco Locatelli and Fabio Schoen and Marco Sciandrone},
  journal={Comp. Opt. and Appl.},
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… CONTINUE READING
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January). Global trajectory optimization: can we prune the solution space when considering deep space manoeuvres? (Final Report). ESA

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