An Analysis of Training Models to Evolve Heuristics for the Travelling Salesman Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{gil-gala:2023:GECCOcomp,
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author = "Francisco Javier {Gil Gala} and Marko Durasevic and
Mateja Dumic and Rebeka Coric and Domagoj Jakobovic",
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title = "An Analysis of Training Models to Evolve Heuristics
for the Travelling Salesman Problem",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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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",
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pages = "575--578",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming,
hyper-heuristics, travelling salesman problem: Poster",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3590559",
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size = "4 pages",
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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.",
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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