Skip to main content
Top
Published in: Soft Computing 9/2011

01-09-2011 | Original Paper

Depth-control strategies for crossover in tree-based genetic programming

Authors: Huayang Xie, Mengjie Zhang

Published in: Soft Computing | Issue 9/2011

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The standard subtree crossover operator in the tree-based genetic programming (GP) has been considered as problematic. In order to improve the standard subtree crossover, controlling depth of crossover points becomes a research topic. However, the existence of many different and inconsistent crossover depth-control schemes and the possibility of many other depth-control schemes make the identification of good depth-control schemes a challenging problem. This paper aims to investigate general heuristics for making good depth-control schemes for crossover in tree-based GP. It analyses the patterns of depth of crossover points in good predecessor programs of five GP systems that use the standard subtree crossover and four approximations of the optimal crossover operator on three problems in different domains. The analysis results show that an effective depth-control scheme is problem-dependent and evolutionary stage-dependent, and that good crossover events have a strong preference for roots and (less strongly) bottoms of parent program trees. The results also show that some ranges of depths between the roots and the bottoms are also preferred, suggesting that unequal-depth-selection-probability strategies are better than equal-depth-selection-probability strategies.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Footnotes
1
Trees with the minimal number of nodes for their depths.
 
2
Bottom nodes of a program tree are likely leaf nodes but leaf nodes may appear at any depth of a program tree.
 
3
This translates the numeric outputs of a GP classifier into class labels based on the sign of the numeric values.
 
4
Discussions of a large size limit will be covered in Sect. 5.
 
Literature
go back to reference Angeline PJ (1996) An investigation into the sensitivity of genetic programming to the frequency of leaf selection during subtree crossover. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic programming 1996: proceedings of the first annual conference, Stanford University, CA, USA. MIT Press, Cambridge, pp 21–29 Angeline PJ (1996) An investigation into the sensitivity of genetic programming to the frequency of leaf selection during subtree crossover. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic programming 1996: proceedings of the first annual conference, Stanford University, CA, USA. MIT Press, Cambridge, pp 21–29
go back to reference Angeline PJ (1996) Two self-adaptive crossover operators for genetic programming. In: Angeline PJ, Kinnear KE Jr (eds) Advances in genetic programming 2, chap 5. MIT Press, Cambridge, pp 89–110 Angeline PJ (1996) Two self-adaptive crossover operators for genetic programming. In: Angeline PJ, Kinnear KE Jr (eds) Advances in genetic programming 2, chap 5. MIT Press, Cambridge, pp 89–110
go back to reference Banzhaf W, Nordin P, Keller R, Francone FD (1998) Genetic programming—an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann, San FranciscoMATH Banzhaf W, Nordin P, Keller R, Francone FD (1998) Genetic programming—an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann, San FranciscoMATH
go back to reference Birge L, Rozenholc Y (2006) How many bins should be put in a regular histogram. Eur Ser Appl Ind Math Probab Stat 10:24–45MathSciNetMATH Birge L, Rozenholc Y (2006) How many bins should be put in a regular histogram. Eur Ser Appl Ind Math Probab Stat 10:24–45MathSciNetMATH
go back to reference Couchet J, Manrique D, Rios J, Rodriguez-Paton A (2007) Crossover and mutation operators for grammar-guided genetic programming. Soft Comput Fusion Found Methodol Appl 11(10):943–955 Couchet J, Manrique D, Rios J, Rodriguez-Paton A (2007) Crossover and mutation operators for grammar-guided genetic programming. Soft Comput Fusion Found Methodol Appl 11(10):943–955
go back to reference Crawford-Marks R, Spector L (2002) Size control via size fair genetic operators in the PushGP genetic programming system. In: Proceedings of the genetic and evolutionary computation conference, pp 733–739 Crawford-Marks R, Spector L (2002) Size control via size fair genetic operators in the PushGP genetic programming system. In: Proceedings of the genetic and evolutionary computation conference, pp 733–739
go back to reference da Silva SGO (2008) Controlling bloat: individual and population based approaches in genetic programming. PhD thesis, University of Coimbra da Silva SGO (2008) Controlling bloat: individual and population based approaches in genetic programming. PhD thesis, University of Coimbra
go back to reference D’haeseleer P (1994) Context preserving crossover in genetic programming. In: Proceedings of the 1994 IEEE world congress on computational intelligence, vol 1, Orlando, Florida, USA. IEEE Press, USA, pp 256–261 D’haeseleer P (1994) Context preserving crossover in genetic programming. In: Proceedings of the 1994 IEEE world congress on computational intelligence, vol 1, Orlando, Florida, USA. IEEE Press, USA, pp 256–261
go back to reference Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern Part C 40(2):121–144CrossRef Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern Part C 40(2):121–144CrossRef
go back to reference Fernandez F, Tomassini M, Vanneschi L (2003) An empirical study of multipopulation genetic programming. Genet Program Evol Mach 4(1):21–51MATHCrossRef Fernandez F, Tomassini M, Vanneschi L (2003) An empirical study of multipopulation genetic programming. Genet Program Evol Mach 4(1):21–51MATHCrossRef
go back to reference Gathercole C, Ross P (1994) Dynamic training subset selection for supervised learning in genetic programming. In: In Davidor Y, Schwefel H-P, Manner R (eds) Parallel problem solving from nature III. Springer, Berlin, pp 312–321 Gathercole C, Ross P (1994) Dynamic training subset selection for supervised learning in genetic programming. In: In Davidor Y, Schwefel H-P, Manner R (eds) Parallel problem solving from nature III. Springer, Berlin, pp 312–321
go back to reference Gruau F (1996) On using syntactic constraints with genetic programming. Adv Genet Program 2:377–394 Gruau F (1996) On using syntactic constraints with genetic programming. Adv Genet Program 2:377–394
go back to reference Gustafson SM (2004) An analysis of diversity in genetic programming. PhD thesis, University of Nottingham Gustafson SM (2004) An analysis of diversity in genetic programming. PhD thesis, University of Nottingham
go back to reference Harries K, Smith P (1997) Exploring alternative operators and search strategies in genetic programming. In: Proceedings of the second annual conference on genetic programming, Stanford University, CA, USA. Morgan Kaufmann, Menlo Park, pp 147–155 Harries K, Smith P (1997) Exploring alternative operators and search strategies in genetic programming. In: Proceedings of the second annual conference on genetic programming, Stanford University, CA, USA. Morgan Kaufmann, Menlo Park, pp 147–155
go back to reference Haynes TD, Schoenefeld DA, Wainwright RL (1996) Type inheritance in strongly typed genetic programming. Adv Genet Program 2:359–376 Haynes TD, Schoenefeld DA, Wainwright RL (1996) Type inheritance in strongly typed genetic programming. Adv Genet Program 2:359–376
go back to reference Hengproprohm S, Chongstitvatana P (2001) Selective crossover in genetic programming. In: ISCIT international symposium on communications and information technologies, ChiangMai Orchid, ChiangMai Thailand, 14–16 November 2001 Hengproprohm S, Chongstitvatana P (2001) Selective crossover in genetic programming. In: ISCIT international symposium on communications and information technologies, ChiangMai Orchid, ChiangMai Thailand, 14–16 November 2001
go back to reference Howard D, Roberts SC, Brankin R (1999) Target detection in SAR imagery by genetic programming. Adv Eng Softw 30:303–311CrossRef Howard D, Roberts SC, Brankin R (1999) Target detection in SAR imagery by genetic programming. Adv Eng Softw 30:303–311CrossRef
go back to reference Iba H, de Garis H (1996) Extending genetic programming with recombinative guidance. In: Angeline PJ, Kinnear KE Jr (eds) Advances in genetic programming 2, chap 4. MIT Press, Cambridge, pp 69–88 Iba H, de Garis H (1996) Extending genetic programming with recombinative guidance. In: Angeline PJ, Kinnear KE Jr (eds) Advances in genetic programming 2, chap 4. MIT Press, Cambridge, pp 69–88
go back to reference Ito T, Iba H, Sato S (1998) Depth-dependent crossover for genetic programming. In: Proceedings of the 1998 IEEE world congress on computational intelligence, Anchorage, Alaska, USA, 5–9 May 1998. IEEE Press, USA, pp 775–780 Ito T, Iba H, Sato S (1998) Depth-dependent crossover for genetic programming. In: Proceedings of the 1998 IEEE world congress on computational intelligence, Anchorage, Alaska, USA, 5–9 May 1998. IEEE Press, USA, pp 775–780
go back to reference Ito T, Iba H, Sato S (1998) Non-destructive depth-dependent crossover for genetic programming. In: Banzhaf W, Poli R, Schoenauer M, Fogarty TC (eds) Proceedings of the first European workshop on genetic programming. LNCS, vol 1391, Paris, 14–15 April 1998. Springer, Berlin, pp 71–82 Ito T, Iba H, Sato S (1998) Non-destructive depth-dependent crossover for genetic programming. In: Banzhaf W, Poli R, Schoenauer M, Fogarty TC (eds) Proceedings of the first European workshop on genetic programming. LNCS, vol 1391, Paris, 14–15 April 1998. Springer, Berlin, pp 71–82
go back to reference Ito T, Iba H, Sato S (1999) A self-tuning mechanism for depth-dependent crossover. In: Spector L, Langdon WB, O’Reilly U-M, Angeline PJ (eds) Advances in genetic programming 3, chap 16. MIT Press, Cambridge, pp 377–399 Ito T, Iba H, Sato S (1999) A self-tuning mechanism for depth-dependent crossover. In: Spector L, Langdon WB, O’Reilly U-M, Angeline PJ (eds) Advances in genetic programming 3, chap 16. MIT Press, Cambridge, pp 377–399
go back to reference Kim M, Becker YL, Fei P, O’Reilly U-M (2009) Constrained genetic programming to minimize overfitting in stock selection. In: Genetic programming theory and practice VI, chap 12. Springer, Berlin, pp 179–194 Kim M, Becker YL, Fei P, O’Reilly U-M (2009) Constrained genetic programming to minimize overfitting in stock selection. In: Genetic programming theory and practice VI, chap 12. Springer, Berlin, pp 179–194
go back to reference Koza JR (1992) Genetic programming—on the programming of computers by means of natural selection. MIT Press, CambridgeMATH Koza JR (1992) Genetic programming—on the programming of computers by means of natural selection. MIT Press, CambridgeMATH
go back to reference Lang KJ (1995) Hill climbing beats genetic search on a boolean circuit synthesis of Koza’s. In: Proceedings of the twelfth international conference on machine learning, Tahoe City, California, USA, July 1995. Morgan Kaufmann, Menlo Park Lang KJ (1995) Hill climbing beats genetic search on a boolean circuit synthesis of Koza’s. In: Proceedings of the twelfth international conference on machine learning, Tahoe City, California, USA, July 1995. Morgan Kaufmann, Menlo Park
go back to reference Langdon WB (1999) Size fair and homologous tree genetic programming crossovers. In: Proceedings of the genetic and evolutionary computation conference, vol 2, pp 1092–1097 Langdon WB (1999) Size fair and homologous tree genetic programming crossovers. In: Proceedings of the genetic and evolutionary computation conference, vol 2, pp 1092–1097
go back to reference Langdon WB (2000) Size fair and homologous tree crossovers for tree genetic programming. Genet Program Evol Mach 1:95–119MATHCrossRef Langdon WB (2000) Size fair and homologous tree crossovers for tree genetic programming. Genet Program Evol Mach 1:95–119MATHCrossRef
go back to reference Langdon WB, Poli R (2002) Foundations of genetic programming. Springer, BerlinMATH Langdon WB, Poli R (2002) Foundations of genetic programming. Springer, BerlinMATH
go back to reference Loveard T, Ciesielski V (2001) Representing classification problems in genetic programming. In: Proceedings of the congress on evolutionary computation, vol 2. IEEE Press, USA, pp 1070–1077 Loveard T, Ciesielski V (2001) Representing classification problems in genetic programming. In: Proceedings of the congress on evolutionary computation, vol 2. IEEE Press, USA, pp 1070–1077
go back to reference Majeed H, Ryan C (2006) A less destructive, context-aware crossover operator for GP. In: Proceedings of EuroGP 2006. LNCS, vol 3905. Springer, Berlin, pp 36–48 Majeed H, Ryan C (2006) A less destructive, context-aware crossover operator for GP. In: Proceedings of EuroGP 2006. LNCS, vol 3905. Springer, Berlin, pp 36–48
go back to reference Manrique D, Marquez F, Rios J, Rodriguez-Paton A (2005) Grammar based crossover operator in genetic programming. In: Proceedings of the 1st international work-conference on the interplay between natural and artificial computation, part II, pp 252–261 Manrique D, Marquez F, Rios J, Rodriguez-Paton A (2005) Grammar based crossover operator in genetic programming. In: Proceedings of the 1st international work-conference on the interplay between natural and artificial computation, part II, pp 252–261
go back to reference O’Reilly U-M, Oppacher F (1994) Program search with a hierarchical variable length representation: genetic programming, simulated annealing and hill climbing. Lect Notes Comput Sci 866:397–406 O’Reilly U-M, Oppacher F (1994) Program search with a hierarchical variable length representation: genetic programming, simulated annealing and hill climbing. Lect Notes Comput Sci 866:397–406
go back to reference Poli R, Langdon WB (1998) On the search properties of different crossover operators in genetic programming. In: Genetic programming 1998: proceedings of the third annual conference, pp 293–301 Poli R, Langdon WB (1998) On the search properties of different crossover operators in genetic programming. In: Genetic programming 1998: proceedings of the third annual conference, pp 293–301
go back to reference Rosca JP, Ballard DH (1995) Causality in genetic programming. In: Proceedings of the sixth international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 256–263 Rosca JP, Ballard DH (1995) Causality in genetic programming. In: Proceedings of the sixth international conference on genetic algorithms. Morgan Kaufmann, Menlo Park, pp 256–263
go back to reference Sherrah JR, Bogner RE, Bouzerdoum A (1997) The evolutionary pre-processor: automatic feature extraction for supervised classification using genetic programming. In: Proceedings of 2nd international conference on genetic programming, pp 304–312 Sherrah JR, Bogner RE, Bouzerdoum A (1997) The evolutionary pre-processor: automatic feature extraction for supervised classification using genetic programming. In: Proceedings of 2nd international conference on genetic programming, pp 304–312
go back to reference Smart W, Zhang M (2004) Probability based genetic programming for multiclass object classification. In: Proceedings of the 8th Pacific Rim international conference on artificial intelligence, pp 251–261 Smart W, Zhang M (2004) Probability based genetic programming for multiclass object classification. In: Proceedings of the 8th Pacific Rim international conference on artificial intelligence, pp 251–261
go back to reference Song A, Ciesielski V, Williams H (2002) Texture classifiers generated by genetic programming. In: Proceedings of the congress on evolutionary computation. IEEE Press, USA, pp 243–248 Song A, Ciesielski V, Williams H (2002) Texture classifiers generated by genetic programming. In: Proceedings of the congress on evolutionary computation. IEEE Press, USA, pp 243–248
go back to reference Soule T, Foster JA (1997) Code size and depth flows in genetic programming. In: Koza JR, Deb K, Dorigo M, Fogel DB, Garzon M, Iba H, Riolo RL (eds) Genetic programming 1997: proceedings of the second annual conference, Stanford University, CA, USA. Morgan Kaufmann, Menlo Park, pp 313–320 Soule T, Foster JA (1997) Code size and depth flows in genetic programming. In: Koza JR, Deb K, Dorigo M, Fogel DB, Garzon M, Iba H, Riolo RL (eds) Genetic programming 1997: proceedings of the second annual conference, Stanford University, CA, USA. Morgan Kaufmann, Menlo Park, pp 313–320
go back to reference Tackett WA (1993) Genetic programming for feature discovery and image discrimination. In: Proceedings of 5th international conference on genetic algorithms, pp 303–309 Tackett WA (1993) Genetic programming for feature discovery and image discrimination. In: Proceedings of 5th international conference on genetic algorithms, pp 303–309
go back to reference Tackett WA (1994) Recombination, selection, and the genetic construction of computer programs. PhD thesis, University of Southern California, Los Angeles, CA, USA Tackett WA (1994) Recombination, selection, and the genetic construction of computer programs. PhD thesis, University of Southern California, Los Angeles, CA, USA
go back to reference Teller A, Veloso M (1995) A controlled experiment: evolution for learning difficult image classification. In: Proceedings of 7th Portuguese conference on artificial intelligence, pp 165–176 Teller A, Veloso M (1995) A controlled experiment: evolution for learning difficult image classification. In: Proceedings of 7th Portuguese conference on artificial intelligence, pp 165–176
go back to reference Winkeler JF, Manjunath BS (1997) Genetic programming for object detection. In: Proceedings of 2nd international conference on genetic programming, pp 330–335 Winkeler JF, Manjunath BS (1997) Genetic programming for object detection. In: Proceedings of 2nd international conference on genetic programming, pp 330–335
go back to reference Xie H, Zhang M (2009) An analysis and evaluation of the saving capability and feasibility of backward-chaining evolutionary algorithms. In: The fourth Australian conference on artificial life. LNAI, vol 5865. Springer, Berlin, pp 63–72 Xie H, Zhang M (2009) An analysis and evaluation of the saving capability and feasibility of backward-chaining evolutionary algorithms. In: The fourth Australian conference on artificial life. LNAI, vol 5865. Springer, Berlin, pp 63–72
go back to reference Xie H, Zhang M, Andreae P (2006) A study of good predecessor programs for reducing fitness evaluation cost in genetic programming. In: Proceedings of the 2006 IEEE congress on evolutionary computation. IEEE Press, USA, pp 9211–9218 Xie H, Zhang M, Andreae P (2006) A study of good predecessor programs for reducing fitness evaluation cost in genetic programming. In: Proceedings of the 2006 IEEE congress on evolutionary computation. IEEE Press, USA, pp 9211–9218
go back to reference Xie H, Zhang M, Andreae P (2007) An analysis of constructive crossover and selection pressure in genetic programming. In: Proceedings of genetic and evolutionary computation conference, pp 1739–1746 Xie H, Zhang M, Andreae P (2007) An analysis of constructive crossover and selection pressure in genetic programming. In: Proceedings of genetic and evolutionary computation conference, pp 1739–1746
go back to reference Xie H, Zhang M, Andreae P, Johnston M (2008) Is the not-sampled issue in tournament selection critical? In: Proceedings of IEEE congress on evolutionary computation. IEEE Press, USA, pp 3711–3718 Xie H, Zhang M, Andreae P, Johnston M (2008) Is the not-sampled issue in tournament selection critical? In: Proceedings of IEEE congress on evolutionary computation. IEEE Press, USA, pp 3711–3718
go back to reference Yuen CC (2004) Selective crossover using gene dominance as an adaptive strategy for genetic programming. MSc Intelligent Systems, University College, London, UK Yuen CC (2004) Selective crossover using gene dominance as an adaptive strategy for genetic programming. MSc Intelligent Systems, University College, London, UK
go back to reference Zhang M, Ciesielski V, Andreae P (2003) A domain independent window-approach to multiclass object detection using genetic programming. EURASIP J Appl Signal Process 2003(8):841–859MATHCrossRef Zhang M, Ciesielski V, Andreae P (2003) A domain independent window-approach to multiclass object detection using genetic programming. EURASIP J Appl Signal Process 2003(8):841–859MATHCrossRef
go back to reference Zhang M, Gao X, Lou W (2007) A new crossover operator in genetic programming for object classification. IEEE Trans Syst Man Cybern Part B 37(5):1332–1343CrossRef Zhang M, Gao X, Lou W (2007) A new crossover operator in genetic programming for object classification. IEEE Trans Syst Man Cybern Part B 37(5):1332–1343CrossRef
go back to reference Zhang M, Smart W (2004) Multiclass object classification using genetic programming. In: Applications of evolutionary computing, EvoWorkshops2004. LNCS, vol 3005. Springer, Berlin, pp 369–378 Zhang M, Smart W (2004) Multiclass object classification using genetic programming. In: Applications of evolutionary computing, EvoWorkshops2004. LNCS, vol 3005. Springer, Berlin, pp 369–378
Metadata
Title
Depth-control strategies for crossover in tree-based genetic programming
Authors
Huayang Xie
Mengjie Zhang
Publication date
01-09-2011
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 9/2011
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-011-0700-9

Other articles of this Issue 9/2011

Soft Computing 9/2011 Go to the issue

Premium Partner