Transforming Constraint Programs to Input for Local Search

arXiv (Cornell University) 2026 Preprint Open access

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Authors: Jo Devriendt, Patrick De Causmaecker, Marc Denecker
DOI: 10.48550/arxiv.2605.19671
Cited by: 2

Abstract

Applying local search algorithms to combinatorial optimization problems is not an easy feat. Typically, human intervention is required to compile the constraints to input data for some metaheuristic algorithm. In this paper, we establish a link between symmetry properties of constraint optimization problems and local search neighborhoods, and we use this link to automatically generate neighborhoods from a constraint specification in the context of the IDP system. We evaluate the obtained neighborhoods for six classical optimization problems. The resulting observations support the viability of this technique.

Fields of Study & Subjects

Constraint Satisfaction and Optimization AI-based Problem Solving and Planning Scheduling and Timetabling Solutions

Publication details

Journal / source
arXiv (Cornell University)
Pages
1–16
Publication date
19 May 2026
Language
EN
Document type
Preprint