Novel Ensemble Genetic Programming Hyper-Heuristics for Uncertain Capacitated Arc Routing Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Wang:2019:GECCOb,

• author = "Shaolin Wang and Yi Mei and Mengjie Zhang",
• title = "Novel Ensemble Genetic Programming Hyper-Heuristics for Uncertain Capacitated Arc Routing Problem",
• booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference",
• year = "2019",
• editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre",
• pages = "1093--1101",
• address = "Prague, Czech Republic",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "13-17 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, routing and network design problems, hyper-heuristic, ensemble learning, uncertain capacity arc routing problem",
• isbn13 = "978-1-4503-6111-8",
• DOI = "doi:10.1145/3321707.3321797",
• size = "9 pages",
• abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) is an important problem with many real-world applications. A major challenge in UCARP is to handle the uncertain environment effectively and reduce the recourse cost upon route failures. Genetic Programming Hyper-heuristic (GPHH) has been successfully applied to automatically evolve effective routing policies to make real-time decisions in the routing process. However, most existing studies obtain a single complex routing policy which is hard to interpret. In this paper, we aim to evolve an ensemble of simpler and more interpretable routing policies than a single complex policy. By considering the two critical properties of ensemble learning, i.e., the effectiveness of each ensemble element and the diversity between them, we propose two novel ensemble GP approaches namely DivBaggingGP and DivNichGP. DivBaggingGP evolves the ensemble elements sequentially, while DivNichGP evolves them simultaneously. The experimental results showed that both DivBaggingGP and DivNichGP could obtain more interpretable routing policies than the single complex routing policy. DivNichGP can achieve better test performance than DivBaggingGP as well as the single routing policy evolved by the current state-of-the-art GPHH. This demonstrates the effectiveness of evolving both effective and interpretable routing policies using ensemble learning.",
• notes = "Also known as \cite{3321797} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Shaolin Wang Yi Mei Mengjie Zhang

Citations