Group for Research in Decision Analysis


Optimization of Algorithms with OPAL

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OPAL is a general-purpose system for modeling and solving algorithm optimization problems. OPAL takes an algorithm as input, and as output it suggests parameter values that maximize some user-defined performance measure. In order to achieve this, the user provides a Python script describing how to launch the target algorithm, and defining the performance measure. OPAL then models this question as a blackbox optimization problem which is then solved by a state-of-the-art direct search solver. OPAL handles a wide variety of parameter types, it can exploit multiple processors in parallel at different levels, and can take advantage of a surrogate blackbox.

, 18 pages