Automated Heuristic Design Using Genetic Programming Hyper-heuristic for Uncertain Capacitated Arc Routing Problem

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

@InProceedings{Liu:2017:GECCOb,

• author = "Yuxin Liu and Yi Mei and Mengjie Zhang and Zili Zhang",
• title = "Automated Heuristic Design Using Genetic Programming Hyper-heuristic for Uncertain Capacitated Arc Routing Problem",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4920-8",
• address = "Berlin, Germany",
• pages = "290--297",
• size = "8 pages",
• URL = "http://doi.acm.org/10.1145/3071178.3071185",
• DOI = "doi:10.1145/3071178.3071185",
• acmid = "3071185",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, hyper-heuristic, uncertain capacitated arc routing problem",
• month = "15-19 " # jul,
• abstract = "Uncertain Capacitated Arc Routing Problem (UCARP) is a variant of the well-known CARP. It considers a variety of stochastic factors to reflect the reality where the exact information such as the actual task demand and accessibilities of edges are unknown in advance. Existing works focus on obtaining a robust solution beforehand. However, it is also important to design effective heuristics to adjust the solution in real time. In this paper, we develop a new Genetic Programming-based Hyper-Heuristic (GPHH) for automated heuristic design for UCARP. A novel effective meta-algorithm is designed carefully to address the failures caused by the environment change. In addition, it employs domain knowledge to filter some infeasible candidate tasks for the heuristic function. The experimental results show that the proposed GPHH significantly outperforms the existing GPHH methods and manually designed heuristics. Moreover, we find that eliminating the infeasible and distant tasks in advance can reduce much noise and improve the efficacy of the evolved heuristics. In addition, it is found that simply adding a slack factor to the expected task demand may not improve the performance of the GPHH.",
• notes = "Also known as \cite{Liu:2017:AHD:3071178.3071185} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Yuxin Liu Yi Mei Mengjie Zhang Zili Zhang

Citations