Graph Crossover

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Al Globus and Sean Atsatt and John Lawton and 
                 Todd Wipke",
  title =        "Graph Crossover",
  howpublished = "www",
  year =         "2000",
  month =        "5 " # may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  size =         "15 pages",
  abstract =     "Most genetic algorithms use string or tree
                 representations. To apply genetic algorithms to graphs,
                 a good crossover operator is necessary. We have
                 developed a general-purpose, novel crossover operator
                 for directed and undirected graphs, and used this
                 operator to evolve molecules and circuits. Unlike
                 strings or trees, a single point in the representation
                 cannot divide every possible graph into two parts,
                 because graphs may contain cycles. Thus, the crossover
                 operator is non-trivial. A steady-state, tournament
                 selection genetic algorithm code (JavaGenes) was used
                 test the graph crossover operator. JavaGenes has
                 successfully evolved pharmaceutical drug molecules and
                 simple digital circuits. For example, morphine,
                 cholesterol, and diazepam were successfully evolved by
                 30-60% of runs within 10,000 generations using a
                 population of 1000 molecules. Since representation
                 strongly affects genetic algorithm performance, adding
                 graphs to the evolutionary programmer's bag-of-tricks
                 should be beneficial. Also, since graph evolution
                 operates directly on the phenotype, genotype to
                 phenotype decoding is eliminated.",
  notes =        "see \cite{globus:2001:GECCO}",

Genetic Programming entries for Al Globus Sean Atsatt John Lawton Todd Wipke