Multiobjective Optimization using {GAI} Models

Jean-Philippe Dubus 1 Christophe Gonzales 1 Patrice Perny 1
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This paper deals with multiobjective optimization in the context of multiattribute utility theory. The alternatives (feasible solutions) are seen as elements of a product set of attributes and preferences over solutions are represented by generalized additive decomposable (GAI) utility functions modeling individual preferences or criteria. Due to decomposability, utility vectors attached to solutions can be compiled into a graphical structure closely related to junction trees, the so-called GAI net. We first show how the structure of the GAI net can be used to determine efficiently the exact set of Pareto-optimal solutions in a product set and provide numerical tests on random instances. Since the exact determination of the Pareto set is intractable in worst case, we propose a near admissible algorithm with performance guarantee, exploiting the GAI structure to approximate the set of Pareto optimal solutions. We present numerical experimentations, showing that both utility decomposition and approximation significantly improve resolution times in multiobjective search problems.
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Submitted on : Thursday, March 31, 2016 - 5:00:06 PM
Last modification on : Thursday, March 21, 2019 - 1:00:40 PM


  • HAL Id : hal-01295848, version 1


Jean-Philippe Dubus, Christophe Gonzales, Patrice Perny. Multiobjective Optimization using {GAI} Models. IJCAI'09 International Joint Conference on Artificial Intelligence, Jul 2009, Pasadena, California, United States. IJCAI'09 International Joint Conference on Artificial Intelligence, pp.1902-1907. 〈hal-01295848〉



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