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Machine Learning for Global Optimization

Andrea Cassioli (cassioli***at***dsi.unifi.it)
David Di Lorenzo (dilorenzo***at***dsi.unifi.it)
Marco Locatelli (locatell***at***di.unito.it)
Fabio Schoen (fabio.schoen***at***unifi.it)
Marco Sciandrone (sciandro***at***dsi.unifi.it)

Abstract: 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 local optimum 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.

Keywords: global optimization, machine learning, support vector machines, space trajectory design

Category 1: Global Optimization

Category 2: Applications -- Science and Engineering (Data-Mining )

Citation: Technical report - Global Optimization Laboratory, Universita' degli Studi di Firenze

Download: [PDF]

Entry Submitted: 07/23/2009
Entry Accepted: 08/01/2009
Entry Last Modified: 12/30/2009

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