{"type":"ScholarlyArticle","title":"Integrating Pseudo-Boolean Constraint Reasoning in Multi-Objective Evolutionary Algorithms","authors":[{"name":"Miguel Terra-Neves","slug":"miguel-terra-neves","given":"Miguel","family":"Terra-Neves","affiliation":"INESC-ID / Instituto Superior Técnico , Universidade de Lisboa , Portugal; OutSystems , Portugal"},{"name":"Inês Lynce","slug":"ines-lynce","given":"Inês","family":"Lynce","affiliation":"INESC-ID / Instituto Superior Técnico , Universidade de Lisboa , Portugal"},{"name":"Vasco Manquinho","slug":"vasco-manquinho","given":"Vasco","family":"Manquinho","affiliation":"INESC-ID / Instituto Superior Técnico , Universidade de Lisboa , Portugal"}],"abstract":"Constraint-based reasoning methods thrive in solving problem instances with a tight solution space. On the other hand, evolutionary algorithms are usually effective when it is not hard to satisfy the problem constraints. This dichotomy has been observed in many optimization problems. In the particular case of Multi-Objective Combinatorial Optimization (MOCO), new recently proposed constraint-based algorithms have been shown to outperform more established evolutionary approaches when a given problem instance is hard to satisfy. In this paper, we propose the integration of constraint-based procedures in evolutionary algorithms for solving MOCO. First, a new core-based smart mutation operator is applied to individuals that do not satisfy all problem constraints. Additionally, a new smart improvement operator based on Minimal Correction Subsets is used to improve the quality of the population. Experimental results clearly show that the integration of these operators greatly improves multi-objective evolutionary algorithms MOEA/D and NSGAII. Moreover, even on problem instances with a tight solution space, the newly proposed algorithms outperform the state-of-the-art constraint-based approaches for MOCO.","doi":"10.24963/ijcai.2019/165","url":"https://ijcai.org/proceedings/2019/165","pdf":"https://ijcai.org/proceedings/2019/0165.pdf","markdown":"https://ijcai.org/proceedings/2019/165.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/165","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Constraints and SAT","track":"Main track","number":165,"pages":{"first":1184,"last":1190},"keywords":["Constraints and SAT: SAT: Evaluation and Analysis","Constraints and SAT: SAT: Solvers and Tools","Constraints and SAT: Constraints: Evaluation and Analysis","Constraints and SAT: Constraints: Solvers and Tools","Heuristic Search and Game Playing: Combinatorial Search and Optimisation"],"videos":[],"references":[{"position":1,"raw":"[Bailey and Stuckey, 2005] J. 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