Search-based algorithms, like planners, schedulers and satisfiability solvers, are notorious for having numerous parameters with a wide choice of values that can affect their performance drastically. As a result, the users of these algorithms, who may not be search experts, spend a significant time in tuning the values of the parameters to get acceptable performance on their particular problem domains. In this paper, we present a learning-based approach for automatic tuning of search-based algorithms to help such users. The benefit of our methodology is that it handles diverse parameter types, performs effectively for a broad range of systematic as well as non-systematic search based solvers (the selected parameters could make the algorithms solve up to 100% problems while the bad parameters would lead to none being solved), incorporates user-specified performance criteria (Φ) and is easy to implement Moreover, the selected parameter will satisfy Φ in the first try or the ranked candidates can be used along with Φ to minimize the number of times the parameter settings need to be adjusted until a problem is solved.
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