Memory with Memory in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Riccardo Poli and Nicholas Freitag McPhee and 
                 Luca Citi and Ellery Crane",
  title =        "Memory with Memory in Genetic Programming",
  journal =      "Journal of Artificial Evolution and Applications",
  year =         "2009",
  note =         "Article ID 570606",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1687-6229",
  URL =          "",
  broken =       "",
  DOI =          "doi:10.1155/2009/570606",
  size =         "16 pages",
  abstract =     "We introduce Memory with Memory Genetic Programming
                 (MwM-GP), where we use soft assignments and soft return
                 operations. Instead of having the new value completely
                 overwrite the old value of registers or memory, soft
                 assignments combine such values. Similarly, in soft
                 return operations the value of a function node is a
                 blend between the result of a calculation and
                 previously returned results. In extensive empirical
                 tests, MwM-GP almost always does as well as traditional
                 GP, while significantly outperforming it in several
                 cases. MwM-GP also tends to be far more consistent than
                 traditional GP. The data suggest that MwM-GP works by
                 successively refining an approximate solution to the
                 target problem and that it is much less likely to have
                 truly ineffective code. MwM-GP can continue to improve
                 over time, but it is less likely to get the sort of
                 exact solution that one might find with traditional
  notes =        "Article ID 570606",

Genetic Programming entries for Riccardo Poli Nicholas Freitag McPhee Luca Citi Ellery Fussell Crane