A Less Destructive, Context-aware Crossover Operator for GP

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{eurogp06:MajeedRyan,
  author =       "Hammad Majeed and Conor Ryan",
  title =        "A Less Destructive, Context-aware Crossover Operator
                 for {GP}",
  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
                 Programming",
  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, steepest
                 ascent crossover hill climbing",
  ISBN =         "3-540-33143-3",
  pages =        "36--48",
  DOI =          "doi:10.1007/11729976_4",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "Standard GP crossover is widely accepted as being a
                 largely {\em destructive} operator, creating many poor
                 offspring in the search for better ones. One of the
                 major reasons for its destructiveness is its disrespect
                 for the context of swapped subtrees in their respective
                 parent trees when creating offspring. At times, this
                 hampers GP's performance considerably, and results in
                 populations with {\em low} average fitness values.

                 Many attempts have been made to make it a more
                 constructive crossover, mostly by preserving the
                 context of the selected subtree in the offspring.
                 Although successful at preserving context, none of
                 these methods provide the opportunity to discover new
                 and better contexts for exchanged subtrees.

                 We introduce a context-aware crossover operator which
                 operates by identifying all possible contexts for a
                 subtree, and evaluating each of them. The context that
                 produces the highest fitness is used to create a child
                 which is then passed into the next generation.

                 We have tested its performance on many benchmark
                 problems. It has shown better results than the standard
                 GP crossover operator, using either the same number or
                 fewer individual evaluations. Furthermore, the average
                 fitness of populations using this scheme improves
                 considerably, and programs produced in this way are
                 much smaller than those produced using standard
                 crossover.",
  notes =        "Part of \cite{collet:2006:GP} EuroGP'2006 held in
                 conjunction with EvoCOP2006 and
                 EvoWorkshops2006.

                 Variation in operator frequencies from beginning to end
                 of GP run.",
}

Genetic Programming entries for Hammad Majeed Conor Ryan

Citations