{"type":"ScholarlyArticle","title":"Exploring Structural Similarity in Fitness Landscapes via Graph Data Mining: A Case Study on Number Partitioning Problems","authors":[{"name":"Mingyu Huang","slug":"mingyu-huang","given":"Mingyu","family":"Huang","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China , 611731 , Chengdu , China; Glasgow College , University of Electronic Science and Technology of China ,, 611731 , Chengdu , China"},{"name":"Ke Li","slug":"ke-li","given":"Ke","family":"Li","affiliation":"Department of Computer Science , University of Exeter , EX4 4QF , Exeter , UK"}],"abstract":"One of the most common problem-solving heuristics is by analogy. For a given problem, a solver can be viewed as a strategic walk on its fitness landscape. Thus if a solver works for one problem instance, we expect it will also be effective for other instances whose fitness landscapes essentially share structural similarities with each other. However, due to the black-box nature of combinatorial optimization, it is far from trivial to infer such similarity in real-world scenarios. To bridge this gap, by using local optima network as a proxy of fitness landscapes, this paper proposed to leverage graph data mining techniques to conduct qualitative and quantitative analyses to explore the latent topological structural information embedded in those landscapes. In our experiments, we use the number partitioning problem as the case and our empirical results are inspiring to support the overall assumption of the existence of structural similarity between landscapes within neighboring dimensions. Besides, experiments on simulated annealing demonstrate that the performance of a meta-heuristic solver is similar on structurally similar landscapes.","doi":"10.24963/ijcai.2023/621","url":"https://ijcai.org/proceedings/2023/621","pdf":"https://ijcai.org/proceedings/2023/0621.pdf","markdown":"https://ijcai.org/proceedings/2023/621.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/621","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Search","track":"Main Track","number":621,"pages":{"first":5595,"last":5603},"keywords":["Search: S: Combinatorial search and optimisation","Data Mining: DM: Data visualization","Data Mining: DM: Exploratory data mining","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Benesty et al., 2009] J. 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