Parsimony pressure made easy

GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008

Volumn , Issue , 2008, Pages 1267-1274

  Poli, Riccardo ^a   McPhee, Nicholas Freitag ^b  

^a UNIVERSITY OF ESSEX   (United Kingdom)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Bloat; Genetic programming; Parsimony pressure

Indexed keywords

COMPUTATION THEORY; GENETIC ALGORITHMS; POPULATION STATISTICS;

BLOAT; COMPLETE CONTROL; EVOLUTION EQUATIONS; PRESSURE METHODS; PROGRAM SIZE; TARGET SIZE;

GENETIC PROGRAMMING;

EID: 57349132473     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1389095.1389340     Document Type: Conference Paper

References (35)

1. L. Altenberg. The Schema Theorem and Price's Theorem. In L. D. Whitley and M. D. Vose, editors, Foundations of Genetic Algorithms 3, Estes Park, Colorado, USA, 31 July-2 Aug. 1994. Morgan Kaufmann. Published 1995.
2. P. J. Angeline. An investigation into the sensitivity of genetic programming to the frequency of leaf selection during subtree crossover. In J. R. Koza, et al., editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 21-29, Stanford University, CA, USA, 28-31 July 1996. MIT Press.
3. W. Banzhaf and W. B. Langdon. Some considerations on the reason for bloat. Genetic Programming and Evolvable Machines, 3(1):81-91, Mar. 2002.
4. R. Crawford-Marks and L. Spector. Size control via size fair genetic operators in the PushGP genetic programming system. In W. B. Langdon, et al., editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 733-739, New York, 9-13 July 2002. Morgan Kaufmann Publishers.
5. S. Dignum and R. Poli. Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloat. In D. Thierens, et al., editors, GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, volume 2, pages 1588-1595, London, 7-11 July 2007. ACM Press.
6. A. Ekart and S. Z. Nemeth. Selection based on the pareto nondomination criterion for controlling code growth in genetic programming. Genetic Programming and Evolvable Machines, 2(1):61-73, Mar. 2001.
7. H. Iba. Complexity-based fitness evaluation for variable length representation. Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97, 20 July 1997.
8. H. Iba, H. de Garis, and T. Sato. Genetic programming using a minimum description length principle. In K. E. Kinnear, Jr., editor, Advances in Genetic Programming, chapter 12, pages 265-284. MIT Press, 1994.
9. H. Iba, H. de Garis, and T. Sato. Temporal data processing using genetic programming. In L. Eshelman, editor, Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), pages 279-286, Pittsburgh, PA, USA, 15-19 July 1995. Morgan Kaufmann.
10. K. E. Kinnear, Jr. Evolving a sort: Lessons in genetic programming. In Proceedings of the 1993 International Conference on Neural Networks, volume 2, pages 881-888, San Francisco, USA, 28 Mar.-1 Apr. 1993. IEEE Press.
11. K. E. Kinnear, Jr. Fitness landscapes and difficulty in genetic programming. In Proceedings of the 1994 IEEE World Conference on Computational Intelligence, volume 1, pages 142-147, Orlando, Florida, USA, 27-29 June 1994. IEEE Press.
12. M. Kotanchek, G. Smits, and E. Vladislavleva. Pursuing the pareto paradigm tournaments, algorithm variations & ordinal optimization. In R. L. Riolo, et al., editors, Genetic Programming Theory and Practice IV, volume 5 of Genetic and Evolutionary Computation, chapter 3. Springer, Ann Arbor, 11-13 May 2006.
13. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.
14. W. B. Langdon. The evolution of size in variable length representations. In 1998 IEEE International Conference on Evolutionary Computation, pages 633-638, Anchorage, Alaska, USA, 5-9 May 1998. IEEE Press.
15. W. B. Langdon. Size fair and homologous tree genetic programming crossovers. Genetic Programming and Evolvable Machines, 1(1/2):95-l 19, Apr. 2000.
16. W. B. Langdon and R. Poli. Fitness causes bloat. In P. K. Chawdhry, et al., editors, Soft Computing in Engineering Design and Manufacturing, pages 13-22. Springer-Verlag London, 23-27 June 1997.
17. W. B. Langdon and R. Poli. Foundations of Genetic Programming. Springer-Verlag, 2002.
18. W. B. Langdon, T. Soule, R. Poli, and J. A. Foster. The evolution of size and shape. In L. Spector, et al., editors, Advances in Genetic Programming 3, chapter 8, pages 163-190. MIT Press, Cambridge, MA, USA, June 1999.
19. S. Luke. Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation, 4(3):274-283, Sept. 2000.
20. N. F. McPhee and J. D. Miller. Accurate replication in genetic programming. In L. Eshelman, editor, Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), pages 303-309, Pittsburgh, PA, USA, 15-19 July 1995. Morgan Kaufmann.
21. N. F. McPhee and R. Poli. Using schema theory to explore interactions of multiple operators. In W. B. Langdon, et al., editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 853-860, New York, 9-13 July 2002. Morgan Kaufmann Publishers.
22. R. Poli. General schema theory for genetic programming with subtree-swapping crossover. In Genetic Programming, Proceedings of EuroGP 2001, LNCS, Milan, 18-20 Apr. 2001. Springer-Verlag.
23. R. Poli. A simple but theoretically-motivated method to control bloat in genetic programming. In C. Ryan, et al., editors, Genetic Programming, Proceedings of the 6th European Conference, EuroGP 2003, LNCS, pages 211-223, Essex, UK, 14-16 Apr. 2003. Springer-Verlag.
24. R. Poli, W. B. Langdon, and S. Dignum. On the limiting distribution of program sizes in tree-based genetic programming. In M. Ebner, et al., editors, Proceedings of the 10th European Conference on Genetic Programming, volume 4445 of LNCS, pages 193-204, Valencia, Spain, 11-13 Apr. 2007. Springer.
25. R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008. (With contributions by J. R. Koza).
26. R. Poli and N. F. McPhee. General schema theory for genetic programming with subtree-swapping crossover: Part II. Evolutionary Computation, 11(2): 169-206, June 2003.
27. G. R. Price. Selection and covariance. Nature, 227, August 1:520-521, 1970.
28. J. Rosea. Generality versus size in genetic programming. In J. R. Koza, et al., editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 381-387, Stanford University, CA, USA, 28-31 July 1996. MIT Press.
29. J. Rosea. A probabilistic model of size drift. In R. L. Riolo and B. Worzel, editors, Genetic Programming Theory and Practice, chapter 8, pages 119-136. Kluwer, 2003.
30. J. P. Rosca. Analysis of complexity drift in genetic programming. In J. R. Koza, et al., editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 286-294, Stanford University, CA, USA, 13-16 July 1997. Morgan Kaufmann.
31. J. P. Rosca and D. H. Ballard. Complexity drift in evolutionary computation with tree representations. Technical Report NRL5, University of Rochester, Computer Science Department, Rochester, NY, USA, Dec. 1996.
32. J. P. Rosca and D. H. Ballard. Rooted-tree schemata in genetic programming. In L. Spector, et al., editors, Advances in Genetic Programming 3, chapter 11, pages 243-271. MIT Press, Cambridge, MA, USA, June 1999.
33. T. Soule and J. A. Foster. Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation, 6(4):293-309, Winter 1998.
34. T. Soule and J. A. Foster. Removal bias: a new cause of code growth in tree based evolutionary programming. In 1998 IEEE International Conference on Evolutionary Computation, pages 781-186, Anchorage, Alaska, USA, 5-9 May 1998. IEEE Press.
35. B.-T. Zhang and H. Mühlenbein. Balancing accuracy and parsimony in genetic programming. Evolutionary Computation, 3(1): 17-38, 1995.