Immediate transference of global improvements to all individuals in a population in Genetic Programming compared to Automatically Defined Functions for the EVEN-5 PARITY problem   [GP] [ADF]

by

Aler, R.

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Info: Proceedings of the First European Workshop on Genetic Programming (Conference proceedings), 1998, p. 60-70
Keywords:genetic algorithms, genetic programming
Abstract:
Koza has shown how automatically defined functions (ADFs) [ADF] can reduce computational effort [CE] in the GP paradigm. In Koza's ADF, as well as in standard GP, an improvement in a part of a program (an ADF or a main body) can only be transferred via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements found by a single individual to the whole population. A system that implements this idea has been proposed and tested for the EVEN-5-PARITY and EVEN-6-PARITY problems. Results are very encouraging: computational effort [CE] is reduced (compared to Koza's ADFs) and the system seems to be less prone to early stagnation. Finally, our work suggests further research where less extreme approaches to our idea could be tested.
Notes:
EuroGP'98

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BibTex:
@InProceedings{aler:1998:5parity,
  author =       "Ricardo Aler",
  title =        "Immediate transference of global improvements to all
                 individuals in a population in Genetic Programming
                 compared to Automatically Defined Functions for the
                 {EVEN}-5 {PARITY} problem",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "60--70",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "Koza has shown how automatically defined functions
                 (ADFs) can reduce computational effort in the GP
                 paradigm. In Koza's ADF, as well as in standard GP, an
                 improvement in a part of a program (an ADF or a main
                 body) can only be transferred via crossover. In this
                 article, we consider whether it is a good idea to
                 transfer immediately improvements found by a single
                 individual to the whole population. A system that
                 implements this idea has been proposed and tested for
                 the EVEN-5-PARITY and EVEN-6-PARITY problems. Results
                 are very encouraging: computational effort is reduced
                 (compared to Koza's ADFs) and the system seems to be
                 less prone to early stagnation. Finally, our work
                 suggests further research where less extreme approaches
                 to our idea could be tested.",
  notes =        "EuroGP'98",
}