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

Created by W.Langdon from gp-bibliography.bib Revision:1.4009

@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