{"type":"ScholarlyArticle","title":"Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms","authors":[{"name":"Chao Bian","slug":"chao-bian","given":"Chao","family":"Bian","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China"},{"name":"Yawen Zhou","slug":"yawen-zhou","given":"Yawen","family":"Zhou","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China"},{"name":"Miqing Li","slug":"miqing-li","given":"Miqing","family":"Li","affiliation":"School of Computer Science , University of Birmingham , Birmingham B15 2TT , U.K."},{"name":"Chao Qian","slug":"chao-qian","given":"Chao","family":"Qian","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China"}],"abstract":"Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update is a key component in multi-objective EAs (MOEAs), and it is performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the first population-size ranked solutions (based on some selection criteria, e.g., non-dominated sorting, crowdedness and indicators) from the collections of the current population and newly-generated solutions. In this paper, we question this practice. We analytically present that introducing randomness into the population update procedure in MOEAs can be beneficial for the search. More specifically, we prove that the expected running time of a well-established MOEA (SMS-EMOA) for solving a commonly studied bi-objective problem, OneJumpZeroJump, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed stochastic population update method. This work is an attempt to challenge a common practice for the population update in MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area.","doi":"10.24963/ijcai.2023/612","url":"https://ijcai.org/proceedings/2023/612","pdf":"https://ijcai.org/proceedings/2023/0612.pdf","markdown":"https://ijcai.org/proceedings/2023/612.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/612","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":612,"pages":{"first":5513,"last":5521},"keywords":["Search: S: Evolutionary computation"],"videos":[],"references":[{"position":1,"raw":"[Aguirre and Tanaka, 2004] H. 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