An Analysis of Training Models to Evolve Heuristics for the Travelling Salesman Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.7644

@InProceedings{gil-gala:2023:GECCOcomp,

• author = "Francisco Javier {Gil Gala} and Marko Durasevic and Mateja Dumic and Rebeka Coric and Domagoj Jakobovic",
• title = "An Analysis of Training Models to Evolve Heuristics for the Travelling Salesman Problem",
• 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 = "575--578",
• 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, hyper-heuristics, travelling salesman problem: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590559",
• size = "4 pages",
• abstract = "Designing heuristics is an arduous task, usually approached with hyper-heuristic methods such as genetic programming (GP). In this setting, the goal of GP is to evolve new heuristics that generalise well, i.e., that work well on a large number of problems. To achieve this, GP must use a good training model to evolve new heuristics and also to evaluate their generalisation ability. For this reason, dozens of training models have been used in the literature. However, there is a lack of comparison between different models to determine their effectiveness, which makes it difficult to choose the right one. Therefore, in this paper, we compare different training models and evaluate their effectiveness. We consider the well-known Travelling Salesman Problem (TSP) as a case study to analyse the performance of different training models and gain insights about training models. Moreover, this research opens new directions for the future application of hyper-heuristics.",
• 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 Francisco Javier Gil Gala Marko Durasevic Mateja Dumic Rebeka Coric Domagoj Jakobovic

Citations