PyDGGA: distributed GGA for automatic configuration

DOI10.1007/978-3-030-80223-3_2OpenAlexW3184585096MaRDI QIDQ2118281

Carlos Ansótegui, Meinolf Sellmann, Josep Pon, Kevin Tierney

Publication date: 22 March 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-80223-3_2

zbMATH Keywords

satisfiabilityautomatic algorithm configuration

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Computational aspects of satisfiability (68R07)

Related Items (2)

The first AI4TSP competition: learning to solve stochastic routing problems ⋮ PyDGGA

Uses Software

• Paramils
• SMAC
• AClib
• Sparrow2Riss

Cites Work

• MaxSAT by improved instance-specific algorithm configuration
• Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
• ParamILS: An Automatic Algorithm Configuration Framework
• Tuning Parameters of Large Neighborhood Search for the Machine Reassignment Problem
• Unnamed Item

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