Abstract
We present PyDGGA, a Python tool that implements a distributed version of the automatic algorithm configurator GGA, which is a specialized genetic algorithm to find high quality parameters for solvers and algorithms. PyDGGA implements GGA using an event-driven architecture and runs a simulation of future generations of the genetic algorithm to maximize the usage of the available computing resources. Overall, PyDGGA offers a friendly interface to deploy elastic distributed AC scenarios on shared high-performance computing clusters.
This work was partially supported by the MINECO-FEDER project TASSAT3 (TIN2016-76573-C2-2-P) and the MICINNs project PROOFS (PID2019-109137GB-C21).
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Ansótegui, C., Pon, J., Sellmann, M., Tierney, K. (2021). PyDGGA: Distributed GGA for Automatic Configuration. In: Li, CM., Manyà, F. (eds) Theory and Applications of Satisfiability Testing – SAT 2021. SAT 2021. Lecture Notes in Computer Science(), vol 12831. Springer, Cham. https://doi.org/10.1007/978-3-030-80223-3_2
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