Towards Automated Strategies in Satisfiability Modulo Theory

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{GalvezRamirez:2016:EuroGP,

• author = "Nicolas {Galvez Ramirez} and Youssef Hamadi and Eric Monfroy and Frederic Saubion",
• title = "Towards Automated Strategies in Satisfiability Modulo Theory",
• booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming",
• year = "2016",
• month = "30 " # mar # "--1 " # apr,
• editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim",
• series = "LNCS",
• volume = "9594",
• publisher = "Springer Verlag",
• address = "Porto, Portugal",
• pages = "230--245",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, hyper heuristic, SMT, Strategy, Z3, Learning algorithm",
• isbn13 = "978-3-319-30668-1",
• DOI = "doi:10.1007/978-3-319-30668-1_15",
• size = "16 pages",
• abstract = "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.",
• notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016",

}

Genetic Programming entries for Nicolas Galvez Ramirez Youssef Hamadi Eric Monfroy Frederic Saubion

Citations