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Using Cartesian genetic programming to implement function modelling
Abstract
This paper presents a new method which uses Cartesian genetic programming (CGP) in order to implement function modelling. Since Julian F. Miller proposed the method of CGP, the research and development of CGP mainly trends in the design of the circuit application in recent years; very few scholars have the related research of function modelling in this field. Therefore, the most important feature in this paper is that we apply CGP which is originally used for circuit design to implement function modelling. By numerical test experiments and comparison, we find that this method of function modelling is novel and has the comparative advantages and it is intelligent (self-adaptive, self-organising, self-learning, self-healing, etc.) while it can greatly increase the system speed.
Keywords
References
- 1. F. Candida, '‘Gene expression programming: a new adaptive algorithm for solving problems’' (2004) Google Scholar
- 2. S. Harding, J.F. Miller, W. Banzhaf, L. Vanneschi Ed., '‘Self-modifying Cartesian genetic programming: Fibonacci, squares, regression and summing’' (2009) Google Scholar
- 3. H. Iba, M. Iwata, T. Higuchi, T. Higuchi Ed., '‘Machine learning approach to gate-level evolvable hardware’' Evolvable Systems: From Biology to Hardware, LNCS (1996) Google Scholar
- 4. M. Keizer, C. Ryan Ed., T. Soule Ed., M. Keijzer Ed., E. Tsang Ed., R. Poli Ed., E. Costa Ed., '‘Improving symbolic regression with interval arithmetic and linear scales’' Proc. Eur. Conf. Genetic Program (2003) Google Scholar
- 5. J.R. Koza, Genetic Programming. On the Programming of Computers by Means of Natural Selection (1992) Google Scholar
- 6. J.F. Miller, R. Cavill, '‘A multi-chromosome approach to standard and embedded Cartesian genetic programming’' Proceedings of the 2006 Genetic and Evolutionary Computation Conference (GECCO 2006) (2006) Google Scholar
- 7. J.F. Miller, P. Thomson, M. Sipper Ed., '‘Aspects of digital evolution: geometry and learning’' Evolvable Systems: From Biology to Hardware, LNCS (1998) Google Scholar
- 8. J.F. Miller, A. Walker, '‘The automatic acquisition, evolution and re-use of modules in Cartesian genetic programming’' Accepted for publication in IEEE Transactions on Evolutionary Computation (2006) Google Scholar
- 9. J.F. Miller, A. Walker, '‘Predicting prime numbers using Cartesian genetic programming’' Proceedings of 10th European Conference on Genetic Programming (EuroGP 2007), LNCS (2007a) Google Scholar
- 10. J.F. Miller, A. Walker, '‘Solving real-valued optimization problems using Cartesian genetic programming’' Proceedings of the 2007 Genetic and Evolutionary Computation Conference (GECCO 2007) (2007b) Google Scholar
- 11. J.F. Miller, '‘An empirical study of the efficiency of learning Boolean functions using a Cartesian genetic programming approach’' in Proceedings of the Genetic and Evolutionary Computation Conference(GECCO’99) (1999) Google Scholar
- 12. J.F. Miller, P. Thomson, T. Fogarty, Algorithms and Evolution Strategies in Engineering and Computer Science: Recent Advancements and Industrial Applications (1997a) Google Scholar
- 13. J.F. Miller, P. Thomson, T. Fogarty, Designing Electronic Circuits Using Evolutionary Algorithms. Arithmetic Circuits: A Case Study Genetic Algorithms and Evolution Strategies in Engineering and Computer Science (1997b) Google Scholar
- 14. R. Polio, '‘Evolution of graph-like programs with parallel distributed genetic programming’' Genetic Algorithms: Proceedings of the Seventh International Conference (1997) Google Scholar
- 15. R. Salustowicz, J. Schmidhuber, '‘Probabilistic incremental program evolution’' Evolut. Comput. (1997) Google Scholar
- 16. M. Sipper, E. Sanchez, D. Mange, M. Thomasine, A. Perez-Uribe, A. Stauffer, '‘A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems’' IEEE Trans. on Evol. Comp. (1997) Google Scholar
- 17. A. Teller, M. Veloso, '‘PADO: learning tree structured algorithms for orchestration into an object recognition system’' (1995) Google Scholar
- 18. A. Topchy, W.F. Punch, L. Spector Ed., E.D. Goodman Ed., A. Wu Ed., W.B. Langdon Ed., H-M. Voigt Ed., M. Gen Ed., S. Sen Ed., M. Dorigo Ed., S. Pezeshk Ed., M.H. Garzon Ed., E. Burke Ed., '‘Faster genetic programming based on local gradient search of numeric leaf values’' Proc. Genetic Evolut. Comput. Conf. (2001) Google Scholar
- 19. E.J. Vladislavleva, G.F. Smits, D. den Hertog, '‘Order of nonlinearity as a complexity measure for models generated by symbolic regression via Pareto genetic programming’' in IEEE Transactions on Evolutionary Computation (2009) Google Scholar
- 20. J.A. Walker, J.F. Miller, '‘Embeddeded Cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems’' Proceedings of the 2006 Genetic and Evolutionary Computation Conference (GECCO 2006) (2006) Google Scholar
