{"type":"ScholarlyArticle","title":"Local Search with Efficient Automatic Configuration for Minimum Vertex Cover","authors":[{"name":"Chuan Luo","slug":"chuan-luo","given":"Chuan","family":"Luo","affiliation":"Microsoft Research , China; Leiden Institute of Advanced Computer Science , Leiden University , The Netherlands"},{"name":"Holger H. Hoos","slug":"holger-h-hoos","given":"Holger H.","family":"Hoos","affiliation":"Leiden Institute of Advanced Computer Science , Leiden University , The Netherlands; Department of Computer Science , University of British Columbia , Canada"},{"name":"Shaowei Cai","slug":"shaowei-cai","given":"Shaowei","family":"Cai","affiliation":"State Key Laboratory of Computer Science , Institute of Software , Chinese Academy of Sciences , China"},{"name":"Qingwei Lin","slug":"qingwei-lin","given":"Qingwei","family":"Lin","affiliation":"Microsoft Research , China"},{"name":"Hongyu Zhang","slug":"hongyu-zhang","given":"Hongyu","family":"Zhang","affiliation":"The University of Newcastle , Australia"},{"name":"Dongmei Zhang","slug":"dongmei-zhang","given":"Dongmei","family":"Zhang","affiliation":"Microsoft Research , China"}],"abstract":"Minimum vertex cover (MinVC) is a prominent NP-hard problem in artificial intelligence, with considerable importance in applications. Local search solvers define the state of the art in solving MinVC. However, there is no single MinVC solver that works best across all types of MinVC instances, and finding the most suitable solver for a given application poses considerable challenges. In this work, we present a new local search framework for MinVC called MetaVC, which is highly parametric and incorporates many effective local search techniques. Using an automatic algorithm configurator, the performance of MetaVC can be optimized for particular types of MinVC instances. Through extensive experiments, we demonstrate that MetaVC significantly outperforms previous solvers on medium-size hard MinVC instances, and shows competitive performance on large MinVC instances. We further introduce a neural-network-based approach for enhancing the automatic configuration process, by identifying and terminating unpromising configuration runs. Our results demonstrate that MetaVC, when automatically configured using this method, can achieve improvements in the best known solutions for 16 large MinVC instances.","doi":"10.24963/ijcai.2019/180","url":"https://ijcai.org/proceedings/2019/180","pdf":"https://ijcai.org/proceedings/2019/0180.pdf","markdown":"https://ijcai.org/proceedings/2019/180.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/180","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":"Heuristic Search and Game Playing","track":"Main track","number":180,"pages":{"first":1297,"last":1304},"keywords":["Heuristic Search and Game Playing: Heuristic Search and Machine Learning","Heuristic Search and Game Playing: Meta-Reasoning and Meta-heuristics","Heuristic Search and Game Playing: Heuristic Search","Heuristic Search and Game Playing: Combinatorial Search and Optimisation"],"videos":[],"references":[{"position":1,"raw":"[Akiba and Iwata, 2016] Takuya Akiba and Yoichi Iwata. 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