Genetic ProgrammingTowards Automated Strategies in Satisfiability Modulo Theory - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Genetic ProgrammingTowards Automated Strategies in Satisfiability Modulo Theory

Résumé

SMT solvers include many heuristic components in order to ease the theorem proving process for different logics and problems. Handling these heuristics is a non-trivial task requiring specific knowledge of many theories that even a SMT solver developer may be unaware of. This is the first barrier to break in order to allow end-users to control heuristics aspects of any SMT solver and to successfully build a strategy for their own purposes. We present a first attempt for generating an automatic selection of heuristics in order to improve SMT solver efficiency and to allow end-users to take better advantage of solvers when unknown problems are faced. Evidence of improvement is shown and the basis for future works with evolutionary and/or learning-based algorithms are raised.

Fichier non déposé

Dates et versions

hal-01449594 , version 1 (30-01-2017)

Identifiants

Citer

Nicolás Gálvez Ramírez, Youssef Hamadi, Eric Monfroy, Frédéric Saubion. Genetic ProgrammingTowards Automated Strategies in Satisfiability Modulo Theory. Genetic Programming - 19th European Conference, EuroGP 2016, 2016, Porto, Portugal. pp.230-245, ⟨10.1007/978-3-319-30668-1_15⟩. ⟨hal-01449594⟩

Collections

UNIV-ANGERS LERIA
37 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More