Iterative Prototype Optimisation with Evolved Improvement Steps

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Jiri Kubalik and Jan Faigl",
  title =        "Iterative Prototype Optimisation with Evolved
                 Improvement Steps",
  editor =       "Pierre Collet and Marco Tomassini and Marc Ebner and 
                 Steven Gustafson and Anik\'o Ek\'art",
  booktitle =    "Proceedings of the 9th European Conference on Genetic
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3905",
  year =         "2006",
  address =      "Budapest, Hungary",
  month =        "10 - 12 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-33143-3",
  pages =        "154--165",
  DOI =          "doi:10.1007/11729976_14",
  bibsource =    "DBLP,",
  abstract =     "Evolutionary algorithms have already been more or less
                 successfully applied to a wide range of optimisation
                 problems. Typically, they are used to evolve a
                 population of complete candidate solutions to a given
                 problem, which can be further refined by some
                 problem-specific heuristic algorithm. In this paper, we
                 introduce a new framework called {\it Iterative
                 Prototype Optimisation with Evolved Improvement Steps}.
                 This is a general optimisation framework, where an
                 initial prototype solution is being improved iteration
                 by iteration. In each iteration, a sequence of
                 actions/operations, which improves the current
                 prototype the most, is found by an evolutionary
                 algorithm. The proposed algorithm has been tested on
                 problems from two different optimisation problem
                 domains - binary string optimisation and the travelling
                 salesman problem. Results show that the concept can be
                 used to solve hard problems of big size reliably
                 achieving comparably good or better results than
                 classical evolutionary algorithms and other selected
  notes =        "Part of \cite{collet:2006:GP} EuroGP'2006 held in
                 conjunction with EvoCOP2006 and EvoWorkshops2006",

Genetic Programming entries for Jiri Kubalik Jan Faigl