Table of Contents
Journal of Artificial Evolution and Applications
Volume 2009, Article ID 570606, 16 pages
Research Article

Memory with Memory in Genetic Programming

1School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK
2Division of Science and Mathematics, University of Minnesota, Morris, MN 56267, USA

Received 24 April 2009; Accepted 20 May 2009

Academic Editor: Leonardo Vanneschi

Copyright © 2009 Riccardo Poli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 GP.