Genetic algorithm parameter sets for line labelling

Academic Article


  • This paper concerns the use of genetic algorithms for line labelling. We are interested in finding an optimal set of algorithm control parameters for this problem. We give results from using a simple genetic algorithm to solve several line labelling problems and discuss the effects of crossover type, population size, crossover rate, mutation rate and iteration limit on algorithm performance. We conclude that the algorithm is very sensitive to mutation rate, and that there is a threshold population size beyond which success rates are very high but that this threshold increases rapidly with the problem size. We recommend that a mutation rate of 0.02 be used in conjunction with a crossover rate of between 0.6 and 0.9. Iteration limit should initially be high, and should only be lowered when the other parameters have been tuned. © 1997 Elsevier Science B.V.
  • Published In

    Digital Object Identifier (doi)

    Author List

  • Myers R; Hancock ER
  • Start Page

  • 1363
  • End Page

  • 1371
  • Volume

  • 18
  • Issue

  • 11-13