Divide and Conquer: Using Single Objective Dispatching Rules to Improve Convergence for Multi-Objective Optimisation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{durasevic:2023:GECCO,

• author = "Marko Durasevic and Francisco Javier Gil-Gala and Domagoj Jakobovic",
• title = "Divide and Conquer: Using Single Objective Dispatching Rules to Improve Convergence for {Multi-Objective} Optimisation",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "1082--1090",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, dispatching rules, scheduling, unrelated machines environment",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583131.3590370",
• size = "9 pages",
• abstract = "Dynamic multi-objective (MO) scheduling problems are encountered in various real-world situations. Due to dynamic events that occur in such problems, one has to resort to using simple constructive heuristics, called dispatching rules (DRs), when tackling them. Since DRs are difficult to design manually there is a lack of existing DRs suitable for solving MO problems. Due to that reason, genetic programming has successfully been applied to evolve DRs specifically for solving MO problems. The process of evolving new DRs is computationally expensive, requiring a significant amount of time to obtain DRs of good quality. For that reason it is worth investigating inwhich ways the convergence of algorithms could be improved. One option is to use DRs previously evolved for optimising individual criteria to initialise the starting population when optimising a MO problem. The goal of this study is to investigate how such an initialisation strategy affects the performance of NSGA-II and NSGA-III when evolving DRs for MO problems. Therefore, 8 MO unrelated machines scheduling problems, containing between 2 and 5 criteria, are considered. The obtained results demonstrate that using previously evolved DRs for single objective optimisation leads to a faster convergence, and in many cases significantly better results.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Marko Durasevic Francisco Javier Gil Gala Domagoj Jakobovic

Citations