Heuristic Evolution with Genetic Programming for Traveling Thief Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Mei:2015:CEC,
  author =       "Yi Mei and Xiaodong Li and Flora Salim and Xin Yao",
  title =        "Heuristic Evolution with Genetic Programming for
                 Traveling Thief Problem",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "2753--2760",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Traveling
                 thief problem, memetic algorithm, interdependent
                 optimization",
  URL =          "http://homepages.ecs.vuw.ac.nz/~yimei/Papers/CEC2015-MeiLiSalimYao.pdf",
  DOI =          "doi:10.1109/CEC.2015.7257230",
  abstract =     "In many real-world applications, one needs to deal
                 with a large multi-silo problem with interdependent
                 silos. In order to investigate the interdependency
                 between silos (subproblems), the Traveling Thief
                 Problem (TTP) was designed as a benchmark problem. TTP
                 is a combination of two well-known sub-problems,
                 Travelling Salesman Problem (TSP) and Knapsack Problem
                 (KP). Although each sub-problem has been intensively
                 investigated, the interdependent combination has been
                 demonstrated to be challenging, and cannot be solved by
                 simply solving the sub-problems separately. The
                 Two-Stage Memetic Algorithm (TSMA) is an effective
                 approach that has decent solution quality and
                 scalability, which consists of a tour improvement stage
                 and an item picking stage. Unlike the traditional TSP
                 local search operators adopted in the former stage, the
                 heuristic for the latter stage is rather intuitive. To
                 further investigate the effect of item picking
                 heuristic, Genetic Programming (GP) is employed to
                 evolve a gain function and a picking function,
                 respectively. The resultant two heuristics were tested
                 on some representative TTP instances, and showed
                 competitive performance, which indicates the potential
                 of evolving more promising heuristics for solving TTP
                 more systematically by GP.",
  notes =        "1300 hrs 15200 CEC2015",
}

Genetic Programming entries for Yi Mei Xiaodong Li Flora Salim Xin Yao

Citations