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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adami, C. (1998). An Introduction to Artificial Life. Berlin: Springer Verlag.
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.
Avise, J. C. (2000). Phylogeography: The History and Formation of Species. Harvard University Press.
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.
Banzhaf. W. (2003). Artificial Regulatory Networks and Genetic Programming. In Genetic Programming, Theory and Practice, Rick. L. Riolo. and Bill. Worzel. (Eds. ). Kluwer.
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.
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.
Downing, K. L. (2001). Reinforced Genetic Programming. Genetic Programming and Evolvable Machines 2(3): 259–288.
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.
Fernandez, F., Tomassini, M. and Vanneschi, L. (2003). An Empirical Study of Multipopulation Genetic Programming. Genetic Programming and Evolvable Machines 4(1): 21–51.
Ferreira, C. (2001). Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13(2).
Hansen, J. V. (2003). Genetic Programming Experiments with Standard and Homologous Crossover Methods. Genetic Programming and Evolvable Machines 4(1): 53–66.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems, second edition. The MIT Press, Cambridge, MA.
Karp, R. M. (2002). Mathematical Challenges from Genomics and Molecular Biology. Notices of the AMS 49(5): 544–553.
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.
Keller, E. F. (2000). The Century of the Gene. Harvard University Press.
Keller, E. F. (2002). Making Sense of Life: Explaining Biological Development with Models, Metaphors, and Machines. Harvard University Press.
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.
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.
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.
Koza, John R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge, MA, USA.
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.
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.
Margulis, L. (2000). Symbiotic Planet. Basic Books.
Maynard Smith, J., and Szathmáry, E. (1999). The Origins of Life. Oxford University Press.
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.
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.
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.
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.
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.
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.
Schmidhuber, J. (1987). Evolutionary principles in self-referential learning. Diploma thesis, Institut für Informatik, Technische Universität München.
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.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media New York
About this chapter
Cite this chapter
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
Download citation
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