Monte-Carlo expression discovery
Cazenave, Tristan (2013), Monte-Carlo expression discovery, International Journal on Artificial Intelligence Tools, 22, 1, p. 1-22. 10.1142/S0218213012500352
Type
Article accepté pour publication ou publiéDate
2013Nom de la revue
International Journal on Artificial Intelligence ToolsVolume
22Numéro
1Éditeur
World Scientific
Pages
1-22
Identifiant publication
Métadonnées
Afficher la notice complèteAuteur(s)
Cazenave, TristanLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Résumé (EN)
Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Pro-gramming evaluates and combines trees to discover expressions that maximize a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from ex-pression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize. [ABSTRACT FROM AUTHOR]Mots-clés
UCT; nested Monte-Carlo search; expression discoveryPublications associées
Affichage des éléments liés par titre et auteur.
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Cazenave, Tristan (2010) Communication / Conférence
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Cazenave, Tristan; Ben Hamida, Sana (2015) Communication / Conférence
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Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility Ben Hamida, Sana; Cazenave, Tristan (2020) Document de travail / Working paper
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