The Effect of Extensive Use of the Mutation Operator on Generalization in Genetic Programming Using Sparse Data Sets

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{banzhaf:1996:mutatation,

• author = "Wolfgang Banzhaf and Frank D. Francone and Peter Nordin",
• title = "The Effect of Extensive Use of the Mutation Operator on Generalization in Genetic Programming Using Sparse Data Sets",
• booktitle = "Parallel Problem Solving from Nature IV, Proceedings of the International Conference on Evolutionary Computation",
• year = "1996",
• editor = "Hans-Michael Voigt and Werner Ebeling and Ingo Rechenberg and Hans-Paul Schwefel",
• series = "LNCS",
• volume = "1141",
• pages = "300--309",
• address = "Berlin, Germany",
• publisher_address = "Heidelberg, Germany",
• month = "22-26 " # sep,
• publisher = "Springer Verlag",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-61723-X",
• DOI = "doi:10.1007/3-540-61723-X_994",
• size = "10 pages",
• abstract = "Ordinarily, Genetic Programming uses little or no mutation. Crossover is the predominant operator. This study tests the effect of a very aggressive use of the mutation operator on the generalisation performance of our Compiling Genetic Programming System (CPGS). We ran our tests on two benchmark classification problems on very sparse training sets. In all, we performed 240 complete runs of population 3000 for each of the problems, varying mutation rate between 5percent and 80percent. We found that increasing the mutation rate can significantly improve the generalization capabilities of GP. The mechanism by which mutation affects the generalization capability of GP is not entirely clear. What is clear is that changing the balance between mutation and crossover effects the course of GP training substantially - for example, increasing mutation greatly extends the number of generations for which the GP system can train before the population converges.",
• notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 machine code GP CGPS used on IRIS, Gaussian 3D and phoneme ELENA classification problems. Iris trivial. On others best performance from 50/50 mix of crossover and mutation.

  Answer extracted via designated hardware register. Stop runs when destructive crossover falls below 10percent (used as convergence indicator). Mutation giving rise to more complex introns. GP premature convergence",

• affiliation = "Dortmund University Department of Computer Science Joseph-vonFraunhofer-Str. 20 44227 Dortmund Germany",

}

Genetic Programming entries for Wolfgang Banzhaf Frank D Francone Peter Nordin

Citations