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An Essay Concerning Human Understanding of Genetic Programming

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Book cover Genetic Programming Theory and Practice

Part of the book series: Genetic Programming Series ((GPEM,volume 6))

Abstract

This chapter presents a personal perspective on the relation between theory and practice in genetic programming. It posits that genetic programming practice (including both applications and technique enhancements) is moving toward biology and that it should continue to do so. It suggests as a consequence that future-oriented genetic programming theory (mathematical theory, developed to help analyze, understand, and predict system behavior) should also borrow, increasingly, from biology. It presents specific challenges for theory vis-à-vis recent technique enhancements, and briefly discusses possibilities for new forms of theory that will be relevant to the leading edge of genetic programming practice.

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References

  • Adami, C. (1998). An Introduction to Artificial Life. Berlin: Springer Verlag.

    Book  Google Scholar 

  • Altenberg, L. (1994). The Evolution of Evolvability in Genetic Programming. In Advances in Genetic Programming, Kinnear, K. E. Jr. (Ed. ), pp. 47–74. The MIT Press.

    Google Scholar 

  • Avise, J. C. (2000). Phylogeography: The History and Formation of Species. Harvard University Press.

    Google Scholar 

  • Back. T. and Schwefel, H. P. (1995). “Evolution Strategies I: Variants and their computational implementation. ” In Genetic Algorithms in Engineering and Computer Science, P. Cuest, et al (Eds. ). John. Wiley & Sons. Ltd.

    Google Scholar 

  • Banzhaf. W. (2003). Artificial Regulatory Networks and Genetic Programming. In Genetic Programming, Theory and Practice, Rick. L. Riolo. and Bill. Worzel. (Eds. ). Kluwer.

    Google Scholar 

  • Barnum, H., Bernstein, H. J. and Spector, L. (2000). Quantum circuits for OR and AND of ORs. Journal of Physics A: Mathematical and General, 33(45): 8047–8057.

    Article  MathSciNet  MATH  Google Scholar 

  • Burke, E., Gustafson, S. and Kendall, G. (2002). A Survey And Analysis Of Diversity Measures in Genetic Programming. In Proceedings of the Genetic and Evolutionary Computation Con-ference (GECCO 2002), W. B. Langdon, et al. (Eds. ), pp. 716–723. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  • Downing, K. L. (2001). Reinforced Genetic Programming. Genetic Programming and Evolvable Machines 2(3): 259–288.

    Article  MATH  Google Scholar 

  • Edmonds, B. (2001). Meta-Genetic Programming: Co-evolving the Operators of Variation. Elektrik, the Turkish Journal of Electrical Engineering and Computer Sciences 9(1): 13–29.

    Google Scholar 

  • Fernandez, F., Tomassini, M. and Vanneschi, L. (2003). An Empirical Study of Multipopulation Genetic Programming. Genetic Programming and Evolvable Machines 4(1): 21–51.

    Article  MATH  Google Scholar 

  • Ferreira, C. (2001). Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13(2).

    Google Scholar 

  • Hansen, J. V. (2003). Genetic Programming Experiments with Standard and Homologous Crossover Methods. Genetic Programming and Evolvable Machines 4(1): 53–66.

    Article  MATH  Google Scholar 

  • Holland, J. H. (1992). Adaptation in Natural and Artificial Systems, second edition. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Karp, R. M. (2002). Mathematical Challenges from Genomics and Molecular Biology. Notices of the AMS 49(5): 544–553.

    MathSciNet  MATH  Google Scholar 

  • Keijzer, M. (1996). Efficiently Representing Populations in Genetic Programming. In Advances in Genetic Programming 2, Angeline, J., and Kinnear, K. E. Jr. (Eds. ), pp. 259–278. The MIT Press.

    Google Scholar 

  • Keller, E. F. (2000). The Century of the Gene. Harvard University Press.

    Google Scholar 

  • Keller, E. F. (2002). Making Sense of Life: Explaining Biological Development with Models, Metaphors, and Machines. Harvard University Press.

    Google Scholar 

  • Keller, R. E. and Banzhaf, W. (2001). Evolution of Genetic Code on a Hard Problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 50–56. Morgan Kaufmann.

    Google Scholar 

  • Kinnear, K. E. Jr. (1994). Alternatives in Automatic Function Definition: A Comparison of Per-formance. In Advances in Genetic Programming, Kinnear, K. E. Jr. (Ed. ), pp. 119–141. The MIT Press.

    Google Scholar 

  • Klein, J. (2002). breve: a 3D Environment for the Simulation of Decentralized Systems and Artificial Life. In Proceedings of Artificial Life VIII, The 8th International Conference on the Simulation and Synthesis of Living Systems, pp. 329–334. The MIT Press.

    Google Scholar 

  • Koza, John R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge, MA, USA.

    MATH  Google Scholar 

  • Koza, J. R. (1995). Gene Duplication to Enable Genetic Programming to Concurrently Evolve Both the Architecture and Work-Performing Steps of a Computer Program. In IJCAI-95 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 734–740. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  • Koza, J. R., Andre, D., Bennett, F. H. III and Keane, M. (1999). Genetic Programming 3: Dar-winian Invention and Problem Solving. Morgan Kaufman, San Francisco, CA, USA.

    Google Scholar 

  • Margulis, L. (2000). Symbiotic Planet. Basic Books.

    Google Scholar 

  • Maynard Smith, J., and Szathmáry, E. (1999). The Origins of Life. Oxford University Press.

    Google Scholar 

  • Nikolaev, N. I., Iba, H. and Slavov, V. (1999). Inductive Genetic Programming with Immune Network Dynamics. In Advances in Genetic Programming 3, L. Spector et al. (Eds), pp. 355–376. The MIT Press.

    Google Scholar 

  • Nordin, P., Banzhaf, W. and Francone, F. D. (1999). Efficient Evolution of Machine Code for CISC Architectures using Instruction Blocks and Homologous Crossover. In Advances in Genetic Programming 3, L. Spector et al. (Eds. ), pp. 275–299. The MIT Press, Cambridge, MA, USA.

    Google Scholar 

  • Poli, R. (2001). General Schema Theory for Genetic Programming with Subtree-Swapping Crossover. In Genetic Programming, Proceedings of EuroGP 2001, J. F. Miller et al. (Eds. ), pp. 143–159. Springer Verlag.

    Google Scholar 

  • Poli, R., and Langdon, W. B. (1999). Sub-machine-code Genetic Programming. In Advances in Genetic Programming 3, Spector, L., et al. (Eds. ), pp. 301–323. The MIT Press.

    Google Scholar 

  • Poli, R., Rowe, J. E. and McPhee, N. F. (2001). Markov Chain Models for GP and Variable-length GAs with Homologous Crossover. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 112–119. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  • Punch, W. F. and Rand, W. M. (2000). GP+Echo+Subsumption = Improved Problem Solving. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), pp. 411–418. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  • Schmidhuber, J. (1987). Evolutionary principles in self-referential learning. Diploma thesis, Institut für Informatik, Technische Universität München.

    Google Scholar 

  • Spector, L. and Robinson, A. (2002). Genetic Programming and Autoconstructive Evolution with the Push Programming Language. Genetic Programming and Evolvable Machines 3(1): 7–40.

    Article  MATH  Google Scholar 

  • Spector, L. and Klein, J. (2002). Evolutionary Dynamics Discovered via Visualization in the BREVE Simulation Environment. In Workshop Proceedings of the 8th International Conference on the Simulation and Synthesis of Living Systems, pp. 163–170. Sydney, Australia: University of New South Wales.

    Google Scholar 

  • Spector, L., Klein, J., Perry, C, and Feinstein, M. (2003). Emergence of Collective Behavior in Evolving Populations of Flying Agents. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2003), Cantu-Paz, E., et al. (Eds. ), pp. 61–73. Springer-Verlag.

    Google Scholar 

  • Spector, L. and Stoffel, K. (1996). Ontogenetic programming. In Genetic Programming 1996: Proceedings of the First Annual Conference (Cambridge, MA, 28–31 July 1996), J. R. Koza (Eds. ), pp. 394–399 The MIT Press.

    Google Scholar 

  • Teller, A. (1999). The Internal Reinforcement of Evolving Algorithms. In Advances in Genetic Programming 3, L. Spector et al. (Eds. ), pp. 325–354. The MIT Press.

    Google Scholar 

  • Wu, A. S. and Garibay, I. (2002). The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm. Genetic Programming and Evolvable Machines 3(2): 157–192.

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Cognitive Science, Hampshire College, Amherst, MA, 01002, USA

    Lee Spector

Authors
  1. Lee Spector
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Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, USA

    Rick Riolo

  2. Genetics Squared, USA

    Bill Worzel

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© 2003 Springer Science+Business Media New York

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Spector, L. (2003). An Essay Concerning Human Understanding of Genetic Programming. In: Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice. Genetic Programming Series, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8983-3_2

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  • DOI: https://doi.org/10.1007/978-1-4419-8983-3_2

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4747-7

  • Online ISBN: 978-1-4419-8983-3

  • eBook Packages: Springer Book Archive

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