Canonical form functions as a simple means for genetic programming to evolve human-interpretable functions

GECCO 2006 - Genetic and Evolutionary Computation Conference

Volumn 1, Issue , 2006, Pages 855-862

  McConaghy, Trent ^a   Gielen, Georges ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Genetic programming; Grammar; Grammatical evolution

Indexed keywords

CONTEXT FREE GRAMMARS; FUNCTIONS; GENETIC ALGORITHMS; MATHEMATICAL MODELS; PROBLEM SOLVING; REGRESSION ANALYSIS;

GRAMMAR; GRAMMATICAL EVOLUTION;

COMPUTATION THEORY;

EID: 33750242199     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143997.1144147     Document Type: Conference Paper

References (33)

1. P.J. Angeline, Two Self-Adaptive Crossover Operators for Genetic Programming, Advances in Genetic Programming 2, MIT Press, Chapter 5, pp. 90-115.
2. W. Banzhaf, Genotype-Phenotype Mapping and Neutral Variation - A Case Study in Genetic Programming, PPSN III, 1994, pp. 322-332.
3. L. Breiman et al. Classification and Regression Trees. Chapman & Hall, New York, 1984.
4. K. Deb et al, A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II, Proc. PPSN VI, Sept. 2000, pp. 849-858.
5. I. Dempsey, M. O'Neill, and A. Brabazon, Meta-Grammar Constant Creation with Grammatical Evolution by Grammatical Evolution, GECCO 2005, 2005.
6. H. Drucker et al., Support vector regression machines, Adv. in Neural Information Proc. Sys. 9, 1997, pp. 155-161.
7. J.H. Friedman, Multivariate Adaptive Regression Splines, Annals of Statistics 19, March 1991, pp. 1-141.
8. C. G. Johnson, Artificial Immune System Programming for Symbolic Regression, EuroGP 2003, Essex, UK, 2003.
9. M. Keijzer, V. Babovic, Dimensionally Aware Genetic Programming, GECCO 1999, vol. 2, 1999.
10. M. Keijzer. Scientific Discovery using Genetic Programming. PhD Thesis, Water & Environment and the Dept. for Mathematical Modelling, T.U. Denmark, 2001.
11. M. Keijzer, Improving Symbolic Regression with Interval Arithmetic and Linear Scaling, EuroGP 2003, LNCS 2610, 2003, pp. 71-83.
12. M. Keijzer, Scaled Symbolic Regression, Genetic Programming and Evolvable Machines, 5(3), Sept. 2004, pp. 259-269.
13. E. Kirshenbaum, H.J. Suermondt, Using Genetic Programming to Obtain a Closed-Form Approximation to a Recursive Function, GECCO 2004, 2004, pp. 543-556.
14. J.R. Koza. Genetic Programming. MIT Press, 1992.
15. W.B. Langdon, R. Poli. Foundations of Genetic Programming. Springer, 2002.
16. P. Langley et al. Scientific Discovery, Computational Explorations of the Creative Process. The MIT Press, 1987.
17. T. McConaghy, T. Eeckelaert, and G. Gielen, CAFFEINE: Template-free Symbolic Model Generation of Analog Circuits via Canonical Form Functions and Genetic Programming, Design Automation and Test Europe, 2005.
18. T. McConaghy, G. Gielen, Analysis of Simulation-Driven Numerical Performance Modeling Techniques for Application to Analog Circuit Optimization, Proc. International Symposium on Circuits and Systems, 2005.
19. T. McConaghy, G. Gielen, Double-Strength CAFFEINE: Fast Template-Free Symbolic Modeling of Analog Circuits via Implicit Canonical Form functions and Explicit Introns, Design Automation and Test Europe, Mar 2006 (in press).
20. B. McKay et al., Non-linear Continuum Regression Using Genetic Programming, in GECCO 1999 (2), pp. 1106-1111.
21. M.D. McKay, R.J. Beckman, and W.J. Conover, A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code, Technometrics, 21(2), 1979, pp. 239-245.
22. P. Nordin, A Compiling Genetic Programming System that Directly Manipulates the Machine Code, Advances in Genetic Programming I, MIT Press, 1994, pp.311-331.
23. M. O'Neill, C. Ryan. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer, 2003.
24. M. O'Neill et al, Pi Grammatical Evolution, GECCO 2004, LNCS 3103, 2004, pp. 617-629.
25. Panait, L., Luke, S., Alternative Bloat Control Methods, GECCO 2004, LNCS 3103, 2004, pp. 630-641.
26. R. Poli, J. Page, W. B. Langdon, Smooth Uniform Crossover, Sub-Machine Code GP and Demes, GECCO 1999, vol 2, July 1999, pp. 1162-1169.
27. D.E. Rumelhart et al., Learning Internal Representations by Error Propagation. Parallel Distributed Processing Vol I, MIT Press, 1986.
28. G. Spencer, Automatic Generation of Programs for Crawling and Walking, Advances in Genetic Programming, MIT Press, Chapter 15, pp. 335-353.
29. A.B. Tickle et. al, Lessons from Past, Current Issues and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks, Neural Hybrid Systems, Springer Verlag, 1999.
30. A. Topchy, W.F. Punch, Faster Genetic Programming Based on Local Gradient Search of Numeric Leaf Values, GECCO 2001, 2001, pp. 155-162.
31. V. Vassilev, J. Miller, The Advantages of Landscape Neutrality in Digital Circuit Evolution, ICES 2000, pp. 252-263.
32. P. A. Whigham, Grammatically-based Genetic Programming, Proc. Workshop on GP: From Theory to Real-World Applications, Tahoe City, CA, 1995.
33. X. Yao, Y. Liu, G. Lin, Evolutionary Programming Made Faster, IEEE Trans. Ev. Comp. 3(2), July 1999, pp. 82-102.