ABSTRACT
In nature, systems with enormous numbers of components (i.e. cells) are evolved from a relatively small genotype. It has not yet been demonstrated that artificial evolution is sufficient to make such a system evolvable. Consequently researchers have been investigating forms of computational development that may allow more evolvable systems. The approaches taken have largely used re-writing, multi- cellularity, or genetic regulation. In many cases it has been difficult to produce general purpose computation from such systems.In this paper we introduce computational development using a form of Cartesian Genetic Programming that includes self-modification operations. One advantage of this approach is that ab initio the system can be used to solve computational problems. We present results on a number of problems and demonstrate the characteristics and advantages that self-modification brings.
- W. Banzhaf, G. Beslon, S. Christensen, J. A. Foster, F. Kps, V. Lefort, J. F. Miller, M. Radman, and J. J. Ramsden. From artificial evolution to computational evolution: A research agenda. Nature Reviews Genetics, 7:729--735, 2006.Google ScholarCross Ref
- W. Banzhaf and J. Miller. The challenge of complexity. In A. Menon, editor, Frontiers in Evolutionary Computation, pages 243--260. Kluwer Academic, 2004.Google ScholarCross Ref
- P. Bentley and S. Kumar. Three ways to grow designs: A comparison of embryogenies for an evolutionary design problem. In W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 35--43, Orlando, Florida, USA, 13-17 1999. Morgan Kaufmann.Google Scholar
- T. G. Gordon and P. J. Bentley. Development brings scalability to hardware evolution. In Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware, pages 272--279, 2005. Google ScholarDigital Library
- F. Gruau. Neural Network Synthesis using Cellular Encoding and the Genetic Algorithm. PhD thesis, Laboratoire de l'Informatique du Parallelisme, Ecole Normale Superieure de Lyon, France, 1994.Google Scholar
- F. Gruau, D. Whitley, and L. Pyeatt. A comparison between cellular encoding and direct encoding for genetic neural networks. In J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 81--89, Stanford University, CA, USA, 28-31 1996. MIT Press. Google ScholarDigital Library
- G. S. Hornby and J. B. Pollack. The advantages of generative grammatical encodings for physical design. In Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, pages 600-607, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, 27--30 2001. IEEE Press.Google ScholarCross Ref
- P. E. Hotz. Comparing direct and developmental encoding schemes in artificial evolution: A case study in evolving lens shapes. In Congress on Evolutionary Computation, CEC 2004, 2004.Google ScholarCross Ref
- G. Kampis. Self-modifying systems in biology and cognitive science, 1991.Google Scholar
- R. Kicinger. Evolutionary development system for structural design. In AAAI Fall Symposium in Developmental Systems, 2006.Google Scholar
- H. Kitano. Designing neural networks using genetic algorithms with graph generation system. Complex Systems, 4(4):461--476, 1990.Google Scholar
- S. Kumar and P. J. Bentley. On Growth, Form and Computers. Academic Press Inc., US, 2003.Google Scholar
- S. Luks and B. Michal. Evolutionary design of arbitrarily large sorting networks using development. Genetic Programming and Evolvable Machines, 6(3):319--347, 2005. Google ScholarDigital Library
- J. F. Miller and P. Thomson. Cartesian genetic programming. In R. Poli, W. Banzhaf, W. B. Langdon, J. F. Miller, P. Nordin, and T. C. Fogarty, editors, Genetic Programming, Proceedings of EuroGP'2000, volume 1802 of LNCS, pages 121--132, Edinburgh, 2000. Springer-Verlag. Google ScholarDigital Library
- J. F. Miller and P. Thomson. A developmental method for growing graphs and circuits. In Proceedings of the 5th International Conference on Evolvable Systems: From Biology to Hardware, volume 2606 of Lecture Notes in Computer Science, pages 93--104. Springer, 2003. Google ScholarDigital Library
- D. Roggen and D. Federici. Multi-cellular development: is there scalability and robustness to gain? In X. Yao, E. Burke, and J. L. et al., editors, proceedings of Parallel Problem Solving from Nature 8, Parallel Problem Solving from Nature (PPSN) 2004, pages 391--400, 2004.Google Scholar
- A. Siddiqi and S. Lucas. A comparison of matrix rewriting versus direct encoding for evolving neural networks. In Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, (Piscataway, NJ, USA), pages 392--397. IEEE Press, 1998.Google ScholarCross Ref
- L. Spector and K. Stoffel. Ontogenetic programming. In J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 394--399, Stanford University, CA, USA, 28-31 1996. MIT Press. Google ScholarDigital Library
- J. A. Walker and J. F. Miller. Investigating the performance of module acquisition in cartesian genetic programming. In GECCO, pages 1649--1656, 2005. Google ScholarDigital Library
Index Terms
- Self-modifying cartesian genetic programming
Recommendations
Self modifying cartesian genetic programming: finding algorithms that calculate pi and e to arbitrary precision
GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computationSelf Modifying Cartesian Genetic Programming (SMCGP) aims to be a general purpose form of developmental genetic programming. The evolved programs are iterated thus allowing an infinite sequence of phenotypes (programs) to be obtained from a single ...
Evolution, development and learning using self-modifying cartesian genetic programming
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computationSelf-Modifying Cartesian Genetic Programming (SMCGP) is a form of genetic programming that integrates developmental (self-modifying) features as a genotype-phenotype mapping. This paper asks: Is it possible to evolve a learning algorithm using SMCGP?
A response to "Genetic programming and emergence"
Banzhaf (Genet Program Evol Mach, 2013 ) raises some interesting points about emergence in the context of genetic programming. However, his central tenet, that genetic programming is an example of top-down emergence, is invalidated by the fact that the ...
Comments