Distributed Hybrid Genetic Programming for Learning Boolean Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Stefan Droste and Dominic Heutelbeck and 
                 Ingo Wegener",
  title =        "Distributed Hybrid Genetic Programming for Learning
                 {Boolean} Functions",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VI 6th
                 International Conference",
  editor =       "Marc Schoenauer and Kalyanmoy Deb and 
                 G{\"u}nter Rudolph and Xin Yao and Evelyne Lutton and 
                 Juan Julian Merelo and Hans-Paul Schwefel",
  year =         "2000",
  publisher =    "Springer Verlag",
  address =      "Paris, France",
  month =        "16-20 " # sep,
  volume =       "1917",
  series =       "LNCS",
  pages =        "181--190",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://ls2-www.cs.uni-dortmund.de/~wegener/papers/Paper93.ps",
  URL =          "http://eldorado.uni-dortmund.de/0x81d98002_0x00034a39",
  URL =          "http://citeseer.ist.psu.edu/322232.html",
  abstract =     "When genetic programming (GP) is used to find programs
                 with Boolean inputs and outputs, ordered binary
                 decision diagrams (OBDDs) are often used successfully.
                 In all known OBDD-based GP-systems the variable
                 ordering, a crucial factor for the size of OBDDs, is
                 preset to an optimal ordering of the known test
                 function. Certainly this cannot be done in practical
                 applications, where the function to learn and hence its
                 optimal variable ordering are unknown. Here, the first
                 GP-system is presented that evolves the variable
                 ordering of the OBDDs and the OBDDs itself by using a
                 distributed hybrid approach. For the experiments
                 presented the unavoidable size increase compared to the
                 optimal variable ordering is quite small. Hence, this
                 approach is a big step towards learning
                 well-generalizing Boolean functions",

Genetic Programming entries for Stefan Droste Dominic Heutelbeck Ingo Wegener