2020/10/08 by Eneko Osaba, Javier Del Ser, Osaba, Eneko +7
Computer Science · #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2010.03917
openalex publication_date 2020/10/08 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Transfer Optimization is an incipient research area dedicated to solving\nmultiple optimization tasks simultaneously. Among the different approaches that\ncan address this problem effectively, Evolutionary Multitasking resorts to\nconcepts from Evolutionary Computation to solve multiple problems within a\nsingle search process. In this paper we introduce a novel adaptive\nmetaheuristic algorithm to deal with Evolutionary Multitasking environments\ncoined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm\n(AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in\norder to exchange knowledge among the optimization problems under\nconsideration. Furthermore, our approach is able to explain by itself the\nsynergies among tasks that were encountered and exploited during the search,\nwhich helps us to understand interactions between related optimization tasks. A\ncomprehensive experimental setup is designed to assess and compare the\nperformance of AT-MFCGA to that of other renowned evolutionary multitasking\nalternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios\ncomposed of 20 instances of 4 combinatorial optimization problems, yielding the\nlargest discrete multitasking environment solved to date. Results are\nconclusive in regard to the superior quality of solutions provided by AT-MFCGA\nwith respect to the rest of the methods, which are complemented by a\nquantitative examination of the genetic transferability among tasks throughout\nthe search process.\n