{"type":"ScholarlyArticle","title":"Feedback-Based Adaptive Crossover-Rate in Evolutionary Computation","authors":[{"name":"Xiaoyuan Guan","slug":"xiaoyuan-guan","given":"Xiaoyuan","family":"Guan","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Key Laboratory of Machine Intelligence and Advanced Computing , MOE , Guangzhou , China"},{"name":"Tianyi Yang","slug":"tianyi-yang","given":"Tianyi","family":"Yang","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China"},{"name":"Chunliang Zhao","slug":"chunliang-zhao","given":"Chunliang","family":"Zhao","affiliation":"School of Data and Science , Qingdao Universityof Science and Technology , Oingdao , China"},{"name":"Yuren Zhou","slug":"yuren-zhou","given":"Yuren","family":"Zhou","affiliation":"School of Software Engineering , Sun Yat-sen University , Zhuhai , China"}],"abstract":"We propose a novel approach to improve multi-objective evolutionary algorithms by modifying crossover operations. Our approach uses a modifiable cross distribution and virtual point to rebalance the probability distribution of all crossover options. This design reduces runtime for typical pseudo-Boolean functions. Experiments and analysis show our approach effectively optimizes bi-objective problems COCZ and LOTZ in Θ(n) time during crossover, outperforming conventional crossover multi-objective evolutionary algorithms (C-MOEA) which require O(n log n) steps. For the tri-objective problem Hierarchical-COCZ, our approach guarantees an expected runtime of Θ(n2 log n), while C-MOEA needs at least Ω(n2 log n) and at most O(n2 log2 n) steps.","doi":"10.24963/ijcai.2024/765","url":"https://ijcai.org/proceedings/2024/765","pdf":"https://ijcai.org/proceedings/2024/0765.pdf","markdown":"https://ijcai.org/proceedings/2024/765.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/765","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Search","track":"Main Track","number":765,"pages":{"first":6923,"last":6930},"keywords":["Machine Learning: ML: Evolutionary learning","Search: S: Evolutionary computation"],"videos":[],"references":[{"position":1,"raw":"[Badkobeh et al., 2014] Golnaz Badkobeh, Per Kristian Lehre, and Dirk Sudholt. 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