Abstract
Geographical distribution is widely held to be a major determinant of evolutionary dynamics. Correspondingly, genetic programming theorists and practitioners have long developed, used, and studied systems in which populations are structured in quasi-geographical ways. Here we show that a remarkably simple version of this idea produces surprisingly dramatic improvements in problem-solving performance on a suite of test problems. The scheme is trivial to implement, in some cases involving little more than the addition of a modulus operation in the population access function, and yet it provides significant benefits on all of our test problems (ten symbolic regression problems and a quantum computing problem). We recommend the broader adoption of this form of “trivial geography” in genetic programming systems.
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References
Andre, David and Koza, John R. (1996). A parallel implementation of genetic programming that achieves super-linear performance. In Arabnia, Hamid R., editor, Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, volume III, pages 1163–1174, Sunnyvale. CSREA.
Avise, J. C. (2000). Phylogeography: The History and Formation of Species. Harvard University Press.
Axelrod, R., Hammond, R. A., and Grafen, A. (2004). Altruism via kin-selection strategies that rely on arbitrary tags with which they coevolve. Evolution, 58(8):1833–1838.
Banzhaf, Wolfgang, Nordin, Peter, Keller, Robert E., and Francone, Frank D. (1998). Genetic Programming — An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann.
Barnum, Howard, Bernstein, Herbert J, and Spector, Lee (2000). Quantum circuits for OR and AND of ORs. Journal of Physics A: Mathematical and General, 33(45):8047–8057.
Bryden, Kenneth M., Ashlock, Daniel A., Corns, Steven, and Willson, Stephen J. (2005). Graph based evolutionary algorithms. IEEE Transactions on Evolutionary Computation, forthcoming.
Burke, Edmund K., Gustafson, Steven, and Kendall, Graham (2004). Diversity in genetic programming: An analysis of measures and correlation with fitness. IEEE Transactions on Evolutionary Computation, 8(1):47–62.
Collins, Robert J. and Jefferson, David R. (1991). Selection in massively parallel genetic algorithms. In Belew, Rick and Booker, Lashon, editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 249–256, San Mateo, CA. Morgan Kaufman.
Crawford-Marks, Raphael and Spector, Lee (2002). Size control via size fair genetic operators in the PushGP genetic programming system. 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 733–739, New York. Morgan Kaufmann Publishers.
D’haeseleer, Patrik and Bluming, Jason (1994). Effects of locality in individual and population evolution. In Kinnear, Jr., Kenneth E., editor, Advances in Genetic Programming, chapter 8, pages 177–198. MIT Press.
Droste, Stefan, Jansen, Thomas, and Wegener, Ingo (1999). Perhaps not a free lunch but at least a free appetizer. 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 1, pages 833–839, Orlando, Florida, USA. Morgan Kaufmann.
Eshel, I. (1972). On the neighbor effect and the evolution of altruistic traits. Theoretical Population Biology, 3:258–277.
Fernandez, Francisco, Tomassini, Marco, and Vanneschi, Leonardo (2003). An empirical study of multipopulation genetic programming. Genetic Programming and Evolvable Machines, 4(1):21–51.
Folino, G., Pizzuti, C, Spezzano, G., Vanneschi, L., and Tomassini, M. (2003). Diversity analysis in cellular and multipopulation genetic programming. In Sarker, Ruhul, Reynolds, Robert, Abbass, Hussein, Tan, Kay Chen, McKay, Bob, Essam, Daryl, and Gedeon, Tom, editors, Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, pages 305–311, Canberra. IEEE Press.
Folino, Gianluigi, Pizzuti, Clara, and Spezzano, Giandomenico (1999). A cellular genetic programming approach to classification. 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 1015–1020, Orlando, Florida, USA. Morgan Kaufmann.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems. MIT Press.
Holland, J. H. (1995). Hidden Order: How Adaptation Builds Complexity. Perseus Books.
Klein, Jon (2002). BREVE: a 3d environment for the simulation of decentralized systems and artificial life. In Standish, R. K., Bedau, M. A., and Abbass, H. A., editors, Proceedings of Artificial Life VIII, the 8th International Conference on the Simulation and Synthesis of Living Systems, pages 329–334. The MIT Press. http://www.spiderland.org/breve/breve-klein-alife2002.pdf.
Koza, John R. (1992). 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.
Lieberman, E., Hauert, C, and Nowak, M. A. (2005). Evolutionary dynamics on graphs. Nature, 433:312–316.
Maruyama, Tsutomu, Hirose, Tetsuya, and Konagaya, Akihiko (1993). A fine-grained parallel genetic algorithm for distributed parallel systems. In Forrest, Stephanie, editor, Proc. of the Fifth Int. Conf. on Genetic Algorithms, pages 184–190, San Mateo, CA. Morgan Kaufmann.
Mayr, Ernst (1942). Systematics and the origin of species from the viewpoint of a zoologist. Columbia University Press.
Nowak, M. A. and May, R. M. (1992). Evolutionary games and spatial chaos. Nature, 359:826–829.
Nowostawski, M. and Poll, R. (1999). Parallel genetic algorithm taxonomy.
Ofria, Charles and Wilke, Claus O. (2004). Avida: A software platform for research in computational evolutionary biology. Artificial Life, 10(2): 191–229.
Pettey, Chrisila C. (1997). Diffusion (cellular) models. In Bäck, Thomas, Fogel, David B., and Michalewicz, Zbigniew, editors, Handbook of Evolutionary Computation, pages C6.4:1–6. Institute of Physics Publishing and Oxford University Press, Bristol, New York.
Ray, Thomas S. (1991). Is it alive or is it GA. In Belew, Richard K. and Booker, Lashon B., editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 527–534, University of California-San Diego, La Jolla, CA, USA. Morgan Kaufmann.
Spector, Lee (2001). Autoconstructive evolution: Push, pushGP, and pushpop. In Spector, Lee, Goodman, Erik D., Wu, Annie, Langdon, W. B., Voigt, Hans-Michael, Gen, Mitsuo, Sen, Sandip, Dorigo, Marco, Pezeshk, Shahram, Garzon, Max H., and Burke, Edmund, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 137–146, San Francisco, California, USA. Morgan Kaufmann.
Spector, Lee (2004). Automatic Quantum Computer Programming: A Genetic Programming Approach, volume 7 of Genetic Programming. Kluwer Academic Publishers, Boston/Dordrecht/New York/London. in press.
Spector, Lee, Barnum, Howard, Bernstein, Herbert J., and Swamy, Nikhil (1999). Finding a better-than-classical quantum AND/OR algorithm using genetic programming. In Angeline, Peter J., Michalewicz, Zbyszek, Schoenauer, Marc, Yao, Xin, and Zalzala, Ali, editors, Proceedings of the Congress on Evolutionary Computation, volume 3, pages 2239–2246, Mayflower Hotel, Washington D.C., USA. IEEE Press.
Spector, Lee and Klein, Jon (2005a). Genetic stability and territorial structure facilitate the evolution of tag-mediated altruism. Artificial Life. Forthcoming.
Spector, Lee and Klein, Jon (2005b). Machine invention of quantum computing circuits by means of genetic programming. In preparation.
Spector, Lee, Klein, Jon, and Keijzer, Maarten (2005). The push3 execution stack and the evolution of control. In Proc. of the Genetic and Evolutionary Computation Conference. Springer-Verlag.
Spector, Lee and Robinson, Alan (2002). Genetic programming and autoconstructive evolution with the push programming language. Genetic Programming and Evolvable Machines, 3(1):7–40.
Wolpert, David H. and Macready, William G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67–82.
Wright, Sewall (1945). Tempo and mode in evolution: a critical review. Ecology, 26:415–419.
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Spector, L., Klein, J. (2006). Trivial Geography in Genetic Programming. In: Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice III. Genetic Programming, vol 9. Springer, Boston, MA. https://doi.org/10.1007/0-387-28111-8_8
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DOI: https://doi.org/10.1007/0-387-28111-8_8
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