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            <title xml:lang="en">Genetic ProgrammingTowards Automated Strategies in Satisfiability Modulo Theory</title>
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                <forename type="first">Nicolás</forename>
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            <idno type="halRefHtml">&lt;i&gt;Genetic Programming - 19th European Conference, EuroGP 2016&lt;/i&gt;, 2016, Porto, Portugal. pp.230-245, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-319-30668-1_15"&gt;&amp;#x27E8;10.1007/978-3-319-30668-1_15&amp;#x27E9;&lt;/a&gt;</idno>
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                <term xml:lang="en">Tuning</term>
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              <p>&lt;p&gt;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.&lt;/p&gt;</p>
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