A Lifelong Learning Hyper-heuristic Method for Bin Packing

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Kevin Sim and Emma Hart and Ben Paechter",
  title =        "A Lifelong Learning Hyper-heuristic Method for Bin
  journal =      "Evolutionary Computation",
  year =         "2015",
  volume =       "23",
  number =       "1",
  pages =        "37--67",
  month =        "Spring",
  keywords =     "genetic algorithms, genetic programming, SNGP, AIS,
                 Hyper-heuristics, artificial immune systems,
                 combinatorial optimisation",
  ISSN =         "1063-6560",
  URL =          "http://www.iidi.napier.ac.uk/binary/dl/file/publicationid/13377145",
  DOI =          "doi:10.1162/EVCO_a_00121",
  size =         "31 pages",
  abstract =     "We describe a novel hyper-heuristic system that
                 continuously learns over time to solve a combinatorial
                 optimisation problem. The system continuously generates
                 new heuristics and samples problems from its
                 environment; and representative problems and heuristics
                 are incorporated into a self-sustaining network of
                 interacting entities inspired by methods in artificial
                 immune systems. The network is plastic in both its
                 structure and content, leading to the following
                 properties: it exploits existing knowledge captured in
                 the network to rapidly produce solutions; it can adapt
                 to new problems with widely differing characteristics;
                 and it is capable of generalising over the problem
                 space. The system is tested on a large corpus of 3,968
                 new instances of 1D bin-packing problems as well as on
                 1,370 existing problems from the literature; it shows
                 excellent performance in terms of the quality of
                 solutions obtained across the datasets and in adapting
                 to dynamically changing sets of problem instances
                 compared to previous approaches. As the network
                 self-adapts to sustain a minimal repertoire of both
                 problems and heuristics that form a representative map
                 of the problem space, the system is further shown to be
                 computationally efficient and therefore scalable.",
  notes =        "'We have described a continuous learning system
                 inspired by previous work in the Artificial Immune
                 System field that is capable of learning to solve a
                 combinatorial optimisation problem, improves its
                 performance over time and adapts to changing
                 environments. The system fuses methods from SNGP
                 [\cite{jackson:2012:EuroGP} \cite{conf/ppsn/Jackson12}]
                 which is used to generate novel heuristics with ideas
                 from immune-network theory...'

                 PMID: 24512321

                 Institute for Informatics and Digital Innovation,
                 Edinburgh Napier University, Edinburgh, EH10, UK

                 Posted Online February 10, 2014.

                 Presented at GECCO 2014 Hot of the Press Track",

Genetic Programming entries for Kevin Sim Emma Hart Ben Paechter