Seeding Genetic Programming Populations

  • W. B. Langdon
  • J. P. Nordin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1802)


We show genetic programming (GP) populations can evolve under the influence of a Pareto multi-objective fitness and program size selection scheme, from “perfect” programs which match the training material to general solutions. The technique is demonstrated with programmatic image compression, two machine learning benchmark problems (Pima Diabetes and Wisconsin Breast Cancer) and an insurance customer profiling task (Benelearn99 data mining).


Breast Cancer Genetic Algorithm Genetic Programming Programming Technique Benchmark Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • W. B. Langdon
    • 1
  • J. P. Nordin
    • 2
  1. 1.Centrum voor Wiskunde en InformaticaAmsterdamThe Netherlands
  2. 2.Chalmers University of TechnologyGøteborgSweden

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