Abstract
Basic genetic programming (GP) techniques allow individuals to take advantage of some basic top-down design principles. In order to evaluate the effectiveness of these techniques, we define a design as an evolutionary frozen root node. We show that GP design converges quickly based primarily on the best individual in the initial random population. This leads to speculation of several mechanisms that could be used to allow basic GP techniques to better incorporate top-down design principles.
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References
Angeline, P. J. and Pollack, J. B. (1993). Evolutionary module acquisition. In Fogel, D. and Atmar, W., editors, Proceedings of the Second Annual Conference on Evolutionary Programming, pages 154–163, La Jolla, CA, USA.
Burke, Edmund, Gustafson, Steven, and Kendall, Graham (2002). A survey and analysis of diversity measures in genetic programming. In Langdon, W. B., Cantú-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M. A., Schultz, A. C, Miller, J. F., Burke, E., and Jonoska, N., editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 716–723, New York. Morgan Kaufmann Publishers.
Daida, Jason M. (2003). What makes a problem GP-hard? In Riolo, Rick L. and Worzel, Bill, editors, Genetic Programming Theory and Practice, chapter 7, pages 99–118. Kluwer.
Daida, Jason M., Hilss, Adam M., Ward, David J., and Long, Stephen L. (2003). Visualizing tree structures in genetic programming. In Cantú-Paz, E., Foster, J. A., Deb, K., Davis, D., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Standish, R., Kendall, G., Wilson, S., Harman, M., Wegener, J., Dasgupta, D., Potter, M. A., Schultz, A. C, Dowsland, K., Jonoska, N., and Miller, J., editors, Genetic and Evolutionary Computation — GECCO-2003, volume 2724 of LNCS, pages 1652–1664, Chicago. Springer-Verlag.
Koza, John R. (1992a). A genetic approach to the truck backer upper problem and the intertwined spiral problem. In Proceedings of IJCNN International Joint Conference on Neural Networks, volume IV, pages 310–318. IEEE Press.
Koza, John R. (1992b). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.
Koza, John R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge Massachusetts.
Lambert, Kenneth A., Nance, Douglas W., and Naps, Thomas L. (1997). Introduction to Computer Science with C++. PWS Publishing Company, Boston, MA, USA.
Langdon, W. B. (2000). Quadratic bloat in genetic programming. In Whitley, Darrell, Goldberg, David, Cantu-Paz, Erick, Spector, Lee, Parmee, Ian, and Beyer, Hans-Georg, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 451–458, Las Vegas, Nevada, USA. Morgan Kaufmann.
Langdon, W. B. and Poli, R. (1998). Why ants are hard. In Koza, John R., Banzhaf, Wolfgang, Chellapilla, Kumar, Deb, Kalyanmoy, Dorigo, Marco, Fogel, David B., Garzon, Max H., Goldberg, David E., Iba, Hitoshi, and Riolo, Rick, editors, Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 193–201, University of Wisconsin, Madison, Wisconsin, USA. Morgan Kaufmann.
Langdon, William B., Soule, Terry, Poli, Riccardo, and Foster, James A. (1999). The evolution of size and shape. In Spector, Lee, Langdon, William B., O’Reilly, Una-May, and Angeline, Peter J., editors, Advances in Genetic Programming 3, chapter 8, pages 163–190. MIT Press, Cambridge, MA, USA.
McPhee, Nicholas Freitag and Hopper, Nicholas J. (1999). Analysis of genetic diversity through population history. In Banzhaf, Wolfgang, Daida, Jason, Eiben, Agoston E., Garzon, Max H., Honavar, Vasant, Jakiela, Mark, and Smith, Robert E., editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1112–1120, Orlando, Florida, USA. Morgan Kaufmann.
Rosca, Justinian (1995). Towards automatic discovery of building blocks in genetic programming. In Siegel, E. V. and Koza, J. R., editors, Working Notes for the AAAI Symposium on Genetic Programming, pages 78–85, MIT, Cambridge, MA, USA. AAAI.
Rosca, Justinian P. and Ballard, Dana H. (1996). Discovery of subroutines in genetic programming. In Angeline, Peter J. and Kinnear, Jr., K. E., editors, Advances in Genetic Programming 2, chapter 9, pages 177–202. MIT Press, Cambridge, MA, USA.
Sastry, Kumara, O’Reilly, Una-May, Goldberg, David E., and Hill, David (2003). Building block supply in genetic programming. In Riolo, Rick L. and Worzel, Bill, editors, Genetic Programming Theory and Practice, chapter 9, pages 137–154. Kluwer.
Soule, Terence and Foster, James A. (1998). Removal bias: a new cause of code growth in tree based evolutionary programming. In 1998 IEEE International Conference on Evolutionary Computation, pages 781–186, Anchorage, Alaska, USA. IEEE Press.
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Hall, J.M., Soule, T. (2005). Does Genetic Programming Inherently Adopt Structured Design Techniques?. In: O’Reilly, UM., Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice II. Genetic Programming, vol 8. Springer, Boston, MA. https://doi.org/10.1007/0-387-23254-0_10
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DOI: https://doi.org/10.1007/0-387-23254-0_10
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