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Genetic Programming Theory and Practice VII pp 1–18Cite as

GPTP 2009: An Example of Evolvability

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Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

This introductory chapter gives a brief description of genetic programming (GP); summarizes current GP algorithm aims, issues, and progress; and finally reviews the contributions of this volume, which were presented at the GP Theory and Practice (GPTP) 2009 workshop.

This year marks a transition wherein the aims of GP algorithms-reasonable resource usage, high quality results, and reliable convergence-are being consistently realized on an impressive variety of “real-world” applications by skilled practitioners in the field. These aims have been realized due to GP researchers’ growing collective understanding of the nature of GP problems which require search across spaces which are massive, multi-modal, and with poor locality, and how that relates to long-discussed GP issues such as bloat and premature convergence. New ways to use and extend GP for improved computational resource usage, quality of results, and reliability are appearing and gaining momentum. These include: reduced resource usage via rationally designed search spaces and fitness functions for specific applications such as induction of implicit functions or modeling stochastic processes arising from bio-networks; improved quality of results by explicitly targeting the interpretability or trustworthiness of the final results; and heightened reliability via consistently introducing new genetic aterial in a structured manner or via coevolution and teaming. These new developments highlight that GP’s challenges have changed from simply “making it work” on smaller problems, to consistently and rapidly getting high-quality results on large real-world problems. GPTP 2009 was a forum to advance GP’s state of the art and its contributions demonstrate how these aims can be met on a variety of difficult problems.

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References

  • Becker, Ying, Fei, Peng, and Lester, Anna M. (2006). Stock selection: An innovative application of genetic programming methodology. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice IV, volume 5 of Genetic and Evolutionary Computation, chapter 12, pages 315–334. Springer, Ann Arbor.

    Google Scholar 

  • Becker, Ying L., Fox, Harold, and Fei, Peng (2007). An empirical study of multiobjective algorithms for stock ranking. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, chapter 14, pages 241–262. Springer, Ann Arbor.

    Google Scholar 

  • Caplan, Michael and Becker, Ying (2004). Lessons learned using genetic programming in a stock picking context. In O'Reilly, Una-May, Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice II, chapter 6, pages 87–102. Springer, Ann Arbor.

    Google Scholar 

  • Chen, Shu-Heng, Zeng, Ren-Jie, and Yu, Tina (2008). Co-evolving trading strategies to analyze bounded rationality in double auction markets. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice VI, Genetic and Evolutionary Computation, chapter 13, pages 195–215. Springer, Ann Arbor.

    Google Scholar 

  • Driscoll, Joseph A., Worzel, Bill, and MacLean, Duncan (2003). Classification of gene expression data with genetic programming. In Riolo, Rick L. and Worzel, Bill, editors, Genetic Programming Theory and Practice, chapter 3, pages 25–42. Kluwer.

    Google Scholar 

  • Futuyma, Douglas (2009). Evolution, Second Edition. Sinauer Associates Inc.

    Google Scholar 

  • Gruau, Frederic (1993). Cellular encoding as a graph grammar. IEE Colloquium on Grammatical Inference: Theory, Applications and Alternatives, (Digest No.092):17/1–10.

    Google Scholar 

  • Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome, and Franklin, James (2001). The Elements of Statistical Learning. Springer, New York, 2nd edition.

    MATH  Google Scholar 

  • Hemberg, Martin (2001). GENR8 - A design tool for surface generation. Master's thesis, Department of Physical Resource Theory, Chalmers University, Sweden.

    Google Scholar 

  • Hornby, Gregory S. (2006). ALPS: the age-layered population structure for reducing the problem of premature convergence. In Keijzer, Maarten, Cattolico, Mike, Arnold, Dirk, Babovic, Vladan, Blum, Christian, Bosman, Peter, Butz, Martin V., Coello Coello, Carlos, Dasgupta, Dipankar, Ficici, Sevan G., Foster, James, Hernandez-Aguirre, Arturo, Hornby, Greg, Lipson, Hod, McMinn, Phil, Moore, Jason, Raidl, Guenther, Rothlauf, Franz, Ryan, Conor, and Thierens, Dirk, editors, GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, volume 1, pages 815–822, Seattle, Washington, USA. ACM Press.

    Google Scholar 

  • Hornby, Gregory S. and Pollack, Jordan B. (2002). Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8(3):223–246.

    Article  Google Scholar 

  • Hu, Jianjun, Goodman, Erik D., and Seo, Kisung (2003). Continuous hierarchical fair competition model for sustainable innovation in genetic programming. In Riolo, Rick L. and Worzel, Bill, editors, Genetic Programming Theory and Practice, chapter 6, pages 81–98. Kluwer.

    Google Scholar 

  • Kantschik, Wolfgang and Banzhaf, Wolfgang (2002). Linear-graph GP—A new GP structure. In Foster, James A., Lutton, Evelyne, Miller, Julian, Ryan, Conor, and Tettamanzi, Andrea G. B., editors, Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002, volume 2278 of LNCS, pages 83–92, Kinsale, Ireland. Springer-Verlag.

    Google Scholar 

  • Keijzer, Maarten (2002). Scientific Discovery using Genetic Programming. PhD thesis, Danish Technical University, Lyngby, Denmark.

    Google Scholar 

  • Kim, Minkyu, Becker, Ying L., Fei, Peng, and O'Reilly, Una-May (2008). Constrained genetic programming to minimize overfitting in stock selection. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice VI, Genetic and Evolutionary Computation, chapter 12, pages 179–195. Springer, Ann Arbor.

    Google Scholar 

  • Kordon, Arthur, Castillo, Flor, Smits, Guido, and Kotanchek, Mark (2005). Application issues of genetic programming in industry. In Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 16, pages 241–258. Springer, Ann Arbor.

    Chapter  Google Scholar 

  • Korns, Michael F. (2006). Large-scale, time-constrained symbolic regression. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice IV, volume 5 of Genetic and Evolutionary Computation, chapter 16, pages –. Springer, Ann Arbor.

    Google Scholar 

  • Korns, Michael F. (2007). Large-scale, time-constrained symbolic regression-classification. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, chapter 4, pages 53–68. Springer, Ann Arbor.

    Google Scholar 

  • Korns, Michael F. and Nunez, Loryfel (2008). Profiling symbolic regression-classification. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice VI, Genetic and Evolutionary Computation, chapter 14, pages 215–229. Springer, Ann Arbor.

    Google Scholar 

  • Kotanchek, Mark, Smits, Guido, and Vladislavleva, Ekaterina (2007). Trustable symoblic regression models. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, chapter 12, pages 203–222. Springer, Ann Arbor.

    Google Scholar 

  • Kotanchek, Mark, Smits, Guido, and Vladislavleva, Ekaterina (2008). Exploiting trustable models via pareto GP for targeted data collection. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice VI, Genetic and Evolutionary Computation, chapter 10, pages 145–163. Springer, Ann Arbor.

    Google Scholar 

  • Koza, John R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.

    MATH  Google Scholar 

  • Lohn, Jason D., Hornby, Gregory S., and Linden, Derek S. (2005). Rapid reevolution of an X-band antenna for NASA's space technology 5 mission. In Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 5, pages 65–78. Springer, Ann Arbor.

    Google Scholar 

  • Mattiussi, Claudio and Floreano, Dario (2007). Analog genetic encoding for the evolution of circuits and networks. IEEE Transactions on Evolutionary Computation, 11(5):596–607.

    Article  Google Scholar 

  • McConaghy, Trent and Gielen, Georges (2005). Genetic programming in industrial analog CAD: Applications and challenges. In Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 19, pages 291–306. Springer, Ann Arbor.

    Google Scholar 

  • McConaghy, Trent, Palmers, Pieter, Gielen, Georges, and Steyaert, Michiel (2007). Genetic programming with reuse of known designs. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, chapter 10, pages 161–186. Springer, Ann Arbor.

    Google Scholar 

  • Miller, Julian Francis and Harding, Simon L. (2008). Cartesian genetic programming. In Ebner, Marc, Cattolico, Mike, van Hemert, Jano, Gustafson, Steven, Merkle, Laurence D., Moore, Frank W., Congdon, Clare Bates, Clack, Christopher D., Moore, Frank W., Rand, William, Ficici, Sevan G., Riolo, Rick, Bacardit, Jaume, Bernado-Mansilla, Ester, Butz, Martin V., Smith, Stephen L., Cagnoni, Stefano, Hauschild, Mark, Pelikan, Martin, and Sastry, Kumara, editors, GECCO-2008 tutorials, pages 2701–2726, Atlanta, GA, USA. ACM.

    Google Scholar 

  • O'Reilly, Una-May and Angeline, Peter J. (1997). Trends in evolutionary methods for program induction. Evolutionary Computation, 5(2):v-ix.

    Article  Google Scholar 

  • O'Reilly, Una-May and Hemberg, Martin (2007). Integrating generative growth and evolutionary computation for form exploration. Genetic Programming and Evolvable Machines, 8(2): 163–186. Special issue on developmental systems.

    Article  Google Scholar 

  • Patel, S. and Clack, C. D. (2007). ALPS evaluation in financial portfolio optimisation. In Srinivasan, Dipti and Wang, Lipo, editors, 2007 IEEE Congress on Evolutionary Computation, pages 813–819, Singapore. IEEE Computational Intelligence Society, IEEE Press.

    Chapter  Google Scholar 

  • Pecenka, Tomas, Sekanina, Lukas, and Kotasek, Zdenek (2008). Evolution of synthetic rtl benchmark circuits with predeþned testability. The 5th Annual (2008) ÒHUMIESÓ Awards.

    Google Scholar 

  • Poli, Riccardo (1997). Evolution of graph-like programs with parallel distributed genetic programming. In Back, Thomas, editor, Genetic Algorithms: Proceedings of the Seventh International Conference, pages 346–353, Michigan State University, East Lansing, MI, USA. Morgan Kaufmann.

    Google Scholar 

  • Poli, Riccardo and Page, Jonathan (2000). Solving high-order boolean parity problems with smooth uniform crossover, sub-machine code GP and demes. Genetic Programming and Evolvable Machines, 1(1/2):37–56.

    Article  MATH  Google Scholar 

  • Ryan, Conor, Nicolau, Miguel, and O'Neill, Michael (2002). Genetic algorithms using grammatical evolution. In Foster, James A., Lutton, Evelyne, Miller, Julian, Ryan, Conor, and Tettamanzi, Andrea G. B., editors, Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002, volume 2278 of LNCS, pages 278–287, Kinsale, Ireland. Springer-Verlag.

    Google Scholar 

  • Slany, Karel (2009). Comparison of CGP and age-layered CGP performance in image operator evolution. In Vanneschi, Leonardo, Gustafson, Steven, Moraglio, Alberto, De Falco, Ivanoe, and Ebner, Marc, editors, Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009, volume 5481 of LNCS, pages 351–361, Tuebingen. Springer.

    Google Scholar 

  • Spector, Lee, Clark, David M., Lindsay, Ian, Barr, Bradford, and Klein, Jon (2008). Genetic programming for finite algebras. In Keijzer, Maarten, Antoniol, Giuliano, Congdon, Clare Bates, Deb, Kalyanmoy, Doerr, Benjamin, Hansen, Nikolaus, Holmes, John H., Hornby, Gregory S., Howard, Daniel, Kennedy, James, Kumar, Sanjeev, Lobo, Fernando G., Miller, Julian Francis, Moore, Jason, Neumann, Frank, Pelikan, Martin, Pollack, Jordan, Sastry, Kumara, Stanley, Kenneth, Stoica, Adrian, Talbi, El-Ghazali, and Wegener, Ingo, editors, GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1291–1298, Atlanta, GA, USA. ACM.

    Chapter  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • Sun, Lei, Hines, Evor L., Green, Roger J., Leeson, Mark S., and Iliescu, D. Daciana (2007). Phase compensating dielectric lens design with genetic programming: Research articles. International Journal of RF and Microwave Computer-Aided Engineering, 17(5):493–504.

    Article  Google Scholar 

  • Terry, Michael A., Marcus, Jonathan, Farrell, Matthew, Aggarwal, Varun, and O'Reilly, Una-May (2006). GRACE: generative robust analog circuit exploration. In Rothlauf, Franz, Branke, Jurgen, Cagnoni, Stefano, Costa, Ernesto, Cotta, Carlos, Drechsler, Rolf, Lutton, Evelyne, Machado, Penousal, Moore, Jason H., Romero, Juan, Smith, George D., Squillero, Giovanni, and Takagi, Hideyuki, editors, Applications of Evolutionary Computing, EvoWorkshops2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, volume 3907 of LNCS, pages 332–343, Budapest. Springer Verlag.

    Google Scholar 

  • Whigham, P. A. (1995). Grammatically-based genetic programming. In Rosca, Justinian P., editor, Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pages 33–41, Tahoe City, California, USA.

    Google Scholar 

  • Willis, Amy, Patel, Suneer, and Clack, Christopher D. (2008). GP age-layer and crossover effects in bid-offer spread prediction. In Keijzer, Maarten, Antoniol, Giuliano, Congdon, Clare Bates, Deb, Kalyanmoy, Doerr, Benjamin, Hansen, Nikolaus, Holmes, John H., Hornby, Gregory S., Howard, Daniel, Kennedy, James, Kumar, Sanjeev, Lobo, Fernando G., Miller, Julian Francis, Moore, Jason, Neumann, Frank, Pelikan, Martin, Pollack, Jordan, Sastry, Kumara, Stanley, Kenneth, Stoica, Adrian, Talbi, El-Ghazali, and Wegener, Ingo, editors, GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1665–1672, Atlanta, GA, USA. ACM.

    Chapter  Google Scholar 

  • Wu, Annie S. and Banzhaf, Wolfgang (1998). Introduction to the special issue: Variable-length representation and noncoding segments for evolutionary algorithms. Evolutionary Computation, 6(4):iii–vi.

    Article  Google Scholar 

  • Yu, Tina, Chen, Shu-Heng, and Kuo, Tzu-Wen (2004). Discovering financial technical trading rules using genetic programming with lambda abstraction. In O'Reilly, Una-May, Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice II, chapter 2, pages 11–30. Springer, Ann Arbor.

    Google Scholar 

  • Zhou, Anjun (2003). Enhance emerging market stock selection. In Riolo, Rick L. and Worzel, Bill, editors, Genetic Programming Theory and Practise, chapter 18, pages 291–302. Kluwer.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA

    Una-May O’Reilly

  2. Solido Design Automation Inc., Saskatoon, Canada

    Trent McConaghy

  3. Center for Study of Complex Systems, University of Michigan, Michigan, USA

    Rick Riolo

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  1. Una-May O’Reilly
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  2. Trent McConaghy
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  3. Rick Riolo
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Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, U.S.A.

    Rick Riolo

  2. Computer Science &, Massachusetts Institute of Technology, Vassar St. 32, Cambridge, 02139, U.S.A.

    Una-May O'Reilly

  3. Solido Design Automation, Inc., Research Drive 102-116, Saskatoon, S7N 3R3, Canada

    Trent McConaghy

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O’Reilly, UM., McConaghy, T., Riolo, R. (2010). GPTP 2009: An Example of Evolvability. In: Riolo, R., O'Reilly, UM., McConaghy, T. (eds) Genetic Programming Theory and Practice VII. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1626-6_1

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  • DOI: https://doi.org/10.1007/978-1-4419-1626-6_1

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