Active learning of Pareto fronts

Campigotto, Paolo and Passerini, Andrea and Battiti, Roberto (2012) Active learning of Pareto fronts. Trento : University of Trento.

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    This work introduces the Active Learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multi-objective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is reduced by an active learning strategy. In particular, the model is learnt from a set of informative training objective vectors. The training objective vectors are approximated Pareto-optimal vectors obtained by solving different scalarized problem instances. The experimental results show that ALP achieves an accurate Pareto front approximation with a lower computational effort than state-of-the-art Estimation of Distribution Algorithms and widely-known genetic techniques.

    Item Type: Departmental Technical Report
    Department or Research center: Information Engineering and Computer Science
    Subjects: Q Science > QA Mathematics > QA063 Problem solving
    Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Uncontrolled Keywords: Multi-objective optimization, Gaussian process regression, active learning, uncertainty sampling
    Additional Information: Supplemental material to a short version submitted to the journal IEEE Transactions on Neural Networks and Learning systems
    Report Number: DISI-13-001
    Repository staff approval on: 13 Feb 2013 12:01

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