A Comparison between Two Evolutionary Hyper-Heuristics for Combinatorial Optimisation

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Richard J. Marshall and Mark Johnston and 
                 Mengjie Zhang",
  title =        "A Comparison between Two Evolutionary Hyper-Heuristics
                 for Combinatorial Optimisation",
  booktitle =    "Proceedings 10th International Conference on Simulated
                 Evolution and Learning, SEAL 2014",
  year =         "2014",
  editor =       "Grant Dick and Will N. Browne and Peter Whigham and 
                 Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and 
                 Yaochu Jin and Xiaodong Li and Yuhui Shi and 
                 Pramod Singh and Kay Chen Tan and Ke Tang",
  volume =       "8886",
  series =       "Lecture Notes in Computer Science",
  pages =        "618--630",
  address =      "Dunedin, New Zealand",
  month =        dec # " 15-18",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-13562-5",
  DOI =          "doi:10.1007/978-3-319-13563-2_52",
  size =         "13 pages",
  abstract =     "Developing and managing a general method of solving
                 combinatorial optimisation problems reduces the need
                 for expensive human experts when solving previously
                 unseen variations to common optimisation problems. A
                 hyper-heuristic provides such a method. Each
                 hyper-heuristic has its own strengths and weaknesses
                 and we research how these properties can be managed. We
                 construct and compare simplified versions of two
                 existing hyper-heuristics (adaptive and grammar-based),
                 and analyse how each handles the trade-off between
                 computation speed and quality of the solution. We test
                 the two hyper-heuristics on seven different problem
                 domains using the HyFlex framework. We conclude that
                 both hyper-heuristics successfully identify and
                 manipulate low-level heuristics to generate good
                 solutions of comparable quality, but the adaptive
                 hyper-heuristic consistently achieves this in a shorter
                 computational time than the grammar based

Genetic Programming entries for Richard J Marshall Mark Johnston Mengjie Zhang