Comparing genetic algortihms and matrix factorization for learning heuristics in constraint solving

Seda Polat Erdeniz, Andrei Popescu*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Knowledge-based recommender systems assist users in the active configuration of complex products. These systems rely on solving Constraint Satisfaction Problems (CSP). In constraint solving, variable and value ordering heuristics help to increase efficiency. Applying such heuristics can increase the performance of CSP solvers. On the other hand, if we apply specific heuristics to similar CSPs, CSP solver performance could be further improved. In previous work, we have proposed novel approaches to learn such heuristics, however, an evaluation in terms of consistency and prediction quality is still lacking. In this paper, we evaluate an compare two proposed approaches to learn heuristics, one relying on Genetic Algorithms and Clustering, and one on Matrix Factorization, on the same problem. Our results provide valuable insights for future research in this domain.

Original languageEnglish
Pages (from-to)21-23
Number of pages3
JournalCEUR Workshop Proceedings
Publication statusPublished - Sept 2021
Event23rd International Configuration Workshop: ConfWS 2021 - Conference Center of Siemens City Vienna (Siemensstraße 90, 1210 Wien), Vienna, Austria
Duration: 16 Sept 202117 Sept 2021

ASJC Scopus subject areas

  • Computer Science(all)

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