Research Article | Open Access
Memory with Memory in Genetic Programming
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.
- A. M. Turing, “The essential turing: seminal writings in computing, logic, philosophy, artificial intelligence, and artificial life plus the secrets of enigma,” in On Computable Numbers, with an Application to the Entscheidungsproblem, pp. 58–87, Oxford University Press, Oxford, UK, 2004.
- J. von Neumann, “First draft of a report on the EDVAC,” Tech. Rep., United States Army Ordnance Department and the University of Pennsylvania, 2008, http://www.virtualtravelog.net/entries/2003-08-TheFirstDraft.pdf.
- R. Poli, W. B. Langdon, and N. F. McPhee, “A field guide to genetic programming,” (With contributions by J. R. Koza), 2008, http://www.gp-field-guide.org.uk.
- W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone, Genetic Programming: An Introduction; On the Automatic Evolution of Computer Programs and Its Applications, Morgan Kaufmann, San Francisco, Calif, USA, 1998.
- A. Teller, “The evolution of mental models,” in Advances in Genetic Programming, K. E. Kinnear Jr., Ed., chapter 9, pp. 199–219, MIT Press, Cambridge, Mass, USA, 1994.
- S. Brave, “Evolving recursive programs for tree search,” in Advances in Genetic Programming 2, P. J. Angeline and K. E. Kinnear Jr., Eds., chapter 10, pp. 203–220, MIT Press, Cambridge, Mass, USA, 1996.
- P. J. Angeline, “An alternative to indexed memory for evolving programs with explicit state representations,” in Proceedings of the 2nd Annual Conference on Genetic Programming, J. R. Koza, K. Deb, M. Dorigo et al., Eds., pp. 423–430, Morgan Kaufmann, Stanford University, CA, USA, July 1997.
- W. B. Langdon, Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!, vol. 1 of Genetic Programming, Kluwer Academic Publishers, Boston, Mass, USA, 1998.
- W. S. Bruce, “Automatic generation of object-oriented programs using genetic programming,” in Proceedings of the 1st Annual Conference on Genetic Programming, J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, Eds., pp. 267–272, MIT Press, Stanford University, Calif, USA, July 1996.
- L. Spector and A. Robinson, “Genetic programming and autoconstructive evolution with the push programming language,” Genetic Programming and Evolvable Machines, vol. 3, no. 1, pp. 7–40, 2002.
- L. Spector, J. Klein, and M. Keijzer, “The push3 execution stack and the evolution of control,” in Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO '05), H.-G. Beyer, U.-M. O'Reilly, D. V. Arnold et al., Eds., vol. 2, pp. 1689–1696, ACM Press, Washington, DC, USA, June 2005.
- E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence : From Natural to Artificial Systems (Santa Fe Institute Studies on the Sciences of Complexity), Oxford University Press, San Diego, Calif, USA, 1999.
- M. Dorigo and T. Stützle, Ant Colony Optimization (Bradford Books), The MIT Press, Cambridge, Mass, USA, 2004.
- R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization,” Swarm Intelligence, vol. 1, no. 1, pp. 33–57, 2007.
- J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, Mass, USA, 1992.
- S. Luke, “Genetic programming produced competitive soccer softbot teams for robocup97,” in Proceedings of the 3rd Annual Conference on Genetic Programming, J. R. Koza, W. Banzhaf, K. Chellapilla et al., Eds., pp. 214–222, University of Wisconsin, Madison, Wis, USA, July 1998.
- L. Spector and S. Luke, “Cultural transmission of information in genetic programming,” in Proceedings of the 1st Annual Conference on Genetic Programming, J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, Eds., pp. 209–214, MIT Press, Stanford University, Calif, USA, July 1996.
- W. Gang and T. Soule, “How to choose appropriate function sets for GP,” in Proceedings of 7th European Conference on Genetic Programming (EuroGP '04), M. Keijzer, U.-M. O'Reilly, S. M. Lucas, E. Costa, and T. Soule, Eds., vol. 3003 of Lecture Notes in Computer Science, pp. 198–207, Springer, Coimbra, Portugal, April 2004.
- S. Besetti and T. Soule, “Function choice, resiliency and growth in genetic programming,” in Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO '05), H.-G. Beyer, U.-M. O'Reilly, D. V. Arnold et al., Eds., vol. 2, pp. 1771–1772, ACM Press, Washington, DC, USA, June 2005.
- S. J. Luck, An Introduction to the Event-Related Potential Technique, MIT Press, Cambridge, Mass, USA, 2005.
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.