Improving complex SMT strategies with learning

DOI10.1111/itor.12650OpenAlexW2922172735WikidataQ128218601 ScholiaQ128218601MaRDI QIDQ6069738

Frédéric Saubion, Unnamed Author, Unnamed Author, Eric Monfroy

Publication date: 17 November 2023

Published in: International Transactions in Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/itor.12650

zbMATH Keywords

local searchgenetic programmingtuningsatisfiability modulo theoriesstrategy languageSBSEZ3

Mathematics Subject Classification ID

Operations research, mathematical programming (90-XX)

Cites Work

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• Parameter setting in evolutionary algorithms.
• Psyche: A Proof-Search Engine Based on Sequent Calculus with an LCF-Style Architecture
• ParamILS: An Automatic Algorithm Configuration Framework
• Simplification by Cooperating Decision Procedures
• Heuristic and exact algorithms for product configuration in software product lines
• The Strategy Challenge in SMT Solving
• Metaheuristics—the metaphor exposed
• The complexity of theorem-proving procedures
• Introduction to evolutionary computing

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