Knowledge and Information Systems

, Volume 21, Issue 1, pp 1–39 | Cite as

A survey and taxonomy of performance improvement of canonical genetic programming

  • Peyman Kouchakpour
  • Anthony Zaknich
  • Thomas Bräunl
Survey Paper

Abstract

The genetic programming (GP) paradigm, which applies the Darwinian principle of evolution to hierarchical computer programs, has been applied with breakthrough success in various scientific and engineering applications. However, one of the main drawbacks of GP has been the often large amount of computational effort required to solve complex problems. Much disparate research has been conducted over the past 25 years to devise innovative methods to improve the efficiency and performance of GP. This paper attempts to provide a comprehensive overview of this work related to Canonical Genetic Programming based on parse trees and originally championed by Koza (Genetic programming: on the programming of computers by means of natural selection. MIT, Cambridge, 1992). Existing approaches that address various techniques for performance improvement are identified and discussed with the aim to classify them into logical categories that may assist with advancing further research in this area. Finally, possible future trends in this discipline and some of the open areas of research are also addressed.

Keywords

Genetic programming Computational effort Efficiency Performance improvement Taxonomy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altenberg L (1994) The evolution of evolvability in genetic programming. In: Kinnear KE (eds) Advances in genetic programming, Chap 3. MIT Press, Cambridge, pp 47–74Google Scholar
  2. 2.
    Altenberg L (1995) Genome growth and the evolution of the genotype-phenotype map. In: Banzhaf W, Eckman FH (eds) Evolution and biocomputation: computational models of evolution. Springer, Berlin, pp 205–259Google Scholar
  3. 3.
    Ando S, Sakamoto E, Iba H (2002) Modelling genetic network by hybrid GP. In: Proceedings of the 2002 congress on evolutionary computation, CEC ’02, vol 1, pp 291–296Google Scholar
  4. 4.
    Andre D, Koza JR (1996) Parallel genetic programming: a scalable implementation using the transputer network architecture. In: Angeline PJ, Kinnear KE (eds) Advances in genetic programming, vol 2, Chap 16. The MIT Press, CambridgeGoogle Scholar
  5. 5.
    Andre D, Koza JR (1998) A parallel implementation of genetic programming that achieves super-linear performance. Inf Sci 106(3–4): 201–218CrossRefGoogle Scholar
  6. 6.
    Angeline PJ, Pollack JB (1992) The evolutionary induction of subroutines. In: The proceedings of the fourteenth annual conference of the cognitive science society, pp 236–241Google Scholar
  7. 7.
    Angeline PJ, Pollack JB (1993) Competitive environments evolve better solutions for complex tasks. In: Proceedings of the fifth international conference on genetic algorithms (ICGA), pp 264–270Google Scholar
  8. 8.
    Angeline PJ, Pollack JB (1994) Coevolving high-level representations. In: Artificial life III, Santa Fe Institute Studies in the Sciences of Complexity, Proceedings, vol XVI, pp 55–72Google Scholar
  9. 9.
    Angeline PJ (1994) Genetic programming: a current snapshot. In: Proceedings of the third annual conference on evolutionary programming. World Scientific, Singapore, pp 224–232Google Scholar
  10. 10.
    Angeline PJ (1996) Two self-adaptive crossover operators for genetic programming. In: Angeline PJ, Kinnear KE (eds) Advances in genetic programming, vol 2. MIT Press, Cambridge, pp 89–109Google Scholar
  11. 11.
    Angeline P (1998) Multiple interacting programs: a representation for evolving complex behaviors. Cybern Syst 29: 779–806MATHCrossRefGoogle Scholar
  12. 12.
    Antolík J, Hsu WH (2005) Evolutionary tree genetic programming. In: GECCO ’05: proceedings of the 2005 conference on Genetic and evolutionary computation, pp 1789–1790Google Scholar
  13. 13.
    Araujo L (2006) Multiobjective genetic programming for natural language parsing and tagging. In: Proceedings of parallel problem solving from nature—PPSN IX. Lecture notes in computer science, pp 433–442Google Scholar
  14. 14.
    Ashlock W, Ashlock D (2005) Single parent genetic programming. In: The 2005 IEEE congress on evolutionary computation, vol 2, pp 1172–1179Google Scholar
  15. 15.
    Badran KMS, Rockett PI (2007) The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programming. In: GECCO ’07: proceedings of the 9th annual conference on Genetic and evolutionary computation, pp 1551–1557Google Scholar
  16. 16.
    Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming: an introduction on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers/Dpunkt-verlag, Menlo Park/HeidelburgGoogle Scholar
  17. 17.
    Besetti S, Soule T (2005) Function choice, resiliency and growth in genetic programming. In: GECCO ’05: proceedings of the 2005 conference on Genetic and evolutionary computation, pp 1771–1772Google Scholar
  18. 18.
    Blickle T, Thiele L (1994) Genetic programming and redundancy. In: Hopf J (ed) Genetic algorithms within the framework of evolutionary computation (Workshop at KI-94), Saarbruicken, pp 33–38Google Scholar
  19. 19.
    Blickle T (1996) Evolving compact solutions in genetic programming: A case study. In: Voigt H-M, Ebeling W, Rechenberg I, Schwefel H-P (eds) PPSN IV. Springer, Heidelberg, pp 564–573Google Scholar
  20. 20.
    Bleuler S, Brack M, Thiele L, Zitzler E (2001) Multiobjective genetic programming: reducing bloat using SPEA2. In: Proceedings of the 2001 congress on evolutionary computation, vol 1, pp 536–543Google Scholar
  21. 21.
    Böhm W, Geyer-Schulz A (1996) Exact uniform initialization for genetic programming. In: Belew RK, Bose M (eds) Foundations of genetic algorithms IV. Morgan Kaufmann, Menlo Park, pp 379–407Google Scholar
  22. 22.
    Boryczka M, Czech ZJ, Wieczorek W (2003) Ant colony programming for approximation problems. In: Proceedings of genetic and evolutionary computation GECCO 2003, PT 1. Lecture notes in computer science, pp 142–143Google Scholar
  23. 23.
    Boryczka M (2005) Eliminating introns in ant colony programming. Fundam Informaticae 68(1–2): 1–19MATHMathSciNetGoogle Scholar
  24. 24.
    Brameier M, Banzhaf W (2001a) A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans Evolut Comput 5(1): 17–26CrossRefGoogle Scholar
  25. 25.
    Brameier M, Banzhaf W (2001b) Evolving teams of predictors with linear genetic programming. Genetic Program Evolvable Mach 2(4): 381–407MATHCrossRefGoogle Scholar
  26. 26.
    Brameier M, Banzhaf W (2007) Linear genetic programming. Springer, HeidelbergMATHGoogle Scholar
  27. 27.
    Cagnoni S, Rivero D, Vanneschi L (2005) A purely evolutionary memetic algorithm as a first step towards symbiotic coevolution. In: The 2005 IEEE congress on evolutionary computation, vol 2, pp 1156–1163Google Scholar
  28. 28.
    Cai XY, Smith SL, Tyrrell AM (2006) Positional independence and recombination in Cartesian genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 351–360Google Scholar
  29. 29.
    Cao HQ, Kang LI, Guo T, Chen YP, de Garis H (2000) A two-level hybrid evolutionary algorithm for modelling one-dimensional dynamic systems by higher-order ODE models. IEEE Trans Syst Man Cybern B 30(2): 351–357CrossRefGoogle Scholar
  30. 30.
    Carbajal SG, Martinez FG (2001) Evolutive introns: a non-costly method of using introns in GP. Genetic Program Evolvable Mach 2: 111–122MATHCrossRefGoogle Scholar
  31. 31.
    Carlos A, Coello C (1999) A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl Inf Syst 1(3): 129–156Google Scholar
  32. 32.
    Cheang SM, Leung KS, Lee KH (2006) Genetic parallel programming: design and implementation. Evolut Comput 14(2): 129–156CrossRefGoogle Scholar
  33. 33.
    Chellapilla K (1997) Evolving computer programs without subtree crossover. IEEE Trans Evolut Comput 1(3): 209–216CrossRefGoogle Scholar
  34. 34.
    Christensen S, Oppacher F (2007) Solving the artificial ant on the Santa Fe trail problem in 20,696 fitness evaluations. In: GECCO ’07: proceedings of the 9th annual conference on Genetic and evolutionary computation, pp 1574–1579Google Scholar
  35. 35.
    Crawford-Marks R, Spector L (2002) Size control via size fair genetic operators in the push GP genetic programming system. In: Proceedings of the genetic and evolutionary computation conference, New York, pp 733–739Google Scholar
  36. 36.
    Collard P, Segapeli JL (1994) Using a double-based genetic algorithm on a population of computer programs. In: Proceedings of 6th international conference of tools with artificial intelligence, pp 418–424Google Scholar
  37. 37.
    Couchet J, Manrique D, Rios J, Rodriguez-Paton A (2007) Crossover and mutation operators for grammar-guided genetic programming. Soft Computing 11(10): 943–955CrossRefGoogle Scholar
  38. 38.
    D’Haesleer P (1994) Context preserving crossover in genetic programming. In: IEEE Proceedings of the 1994 World congress on computational intelligence, Orlando, pp 1:379–407Google Scholar
  39. 39.
    deGaris H (1990) Genetic programming: building artificial nervous systems using genetically programmed neural network modules. In: Porter BW et al (eds) Procedings of seventh international conference on machine learning (ICML-90), pp 132–139Google Scholar
  40. 40.
    De Jong E, Pollack J (2003) Multi-objective Methods for tree size control. Genet Program Evolv Mach 4: 211–233CrossRefGoogle Scholar
  41. 41.
    de Vega FF, Gil GG, Pulido JAG, Guisado JL (2004a) Control of bloat in genetic programming by means of the island model. In: Parallel problem solving from nature—PPSN VIII. Lecture notes in computer science, pp 263–271Google Scholar
  42. 42.
    de Vega FF, Cantu-Paz E, Lopez JI, Manzano T (2004b) Saving resources with plagues in genetic algorithms. In: Parallel problem solving from nature—PPSN VIII. Lecture notes in computer science, pp 272–281Google Scholar
  43. 43.
    Daida JM, Hilss AM (2003) Identifying structural mechanisms. In: Cantú-Paz E. et al (eds) Standard GP, in GECCO. Springer, Heidelberg, pp 1639–1651Google Scholar
  44. 44.
    Daida JM (2006) Characterizing the dynamics of symmetry breaking in genetic programming. In: GECCO ’06: proceedings of the 8th annual conference on Genetic and evolutionary computation, pp 799–806Google Scholar
  45. 45.
    Deb K, Agrawal S, Pratab A, Meyarivan T (2001) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, Kan-GAL report 200001. Indian Institute of Technology, Kanpur, IndiaGoogle Scholar
  46. 46.
    De Falco I, Della Cioppa A, Iazzetta A et al (2005) An evolutionary approach for automatically extracting intelligible classification rules. Knowl Inf Syst 7(2): 179–201CrossRefGoogle Scholar
  47. 47.
    Dignum S, Poli R (2007) Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloat. In: GECCO ’07: proceedings of the 9th annual conference on Genetic and evolutionary computation, pp 1588–1595Google Scholar
  48. 48.
    Dracopoulos DC, Kent S (1996) Speeding up genetic programming: a parallel BSP implementation. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Proceedings of the first annual conference on genetic programming 1996, July 28–31. MIT Press, Cambridge, pp 125–136Google Scholar
  49. 49.
    Du X, Li YQ, Xie DT, Kang LS (2006) A new algorithm of automatic programming: GEGEP. In: Proceedings of simulated evolution and learning. Lecture notes in computer science, pp 292–301Google Scholar
  50. 50.
    Eggermont J, Kok JN, Kosters WA (2004) Detecting and pruning introns for faster decision tree evolution. In: Parallel problem solving from nature—PPSN VIII. Lecture notes in computer science, pp 1071–1080Google Scholar
  51. 51.
    Eiben AE, Smith JE (2003) Introduction to evolutionary computing, 1st edn. Springer, Natural Computing Series, pp 129–151Google Scholar
  52. 52.
    Eskridge BE, Hougen DF (2004a) Memetic crossover for genetic programming: evolution through imitation. In: Proceedings of genetic and evolutionary computation GECCO 2004, pt 2. Lecture notes in computer science, pp 459–470Google Scholar
  53. 53.
    Eskridge BE, Hougen DF (2004b) Imitating success: a memetic crossover operator for genetic programming. In: Congress on evolutionary computation, CEC2004, vol 1, pp 809–815Google Scholar
  54. 54.
    Fernandez F, Tomassini M, Punch WF, Sanchez JM (2000a) Experimental study of multipopulation parallel genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 283–293Google Scholar
  55. 55.
    Fernandez F, Tomassini M, Sanchez JM (2004b) Experimental study of isolated multipopulation genetic programming. In: 26th Annual conference of the IEEE industrial electronics society, vol 4, IECON 2000, pp 2672–2677Google Scholar
  56. 56.
    Fernandez F, Galeano G, Gomez JA, Sanchez JM (2002) Efficient use of computational resources in genetic programming: controlling the bloat phenomenon by means of the island model. In: IECON 02, Industrial Electronics Society. IEEE 2002 28th Annual Conference, vol 3, pp 2520–2524Google Scholar
  57. 57.
    Fernandez F, Vanneschi L, Tomassini M (2003a) The effect of plagues in genetic programming: a study of variable-size populations. In: Proceedings of genetic programming. Lecture notes in computer science, pp 317–326Google Scholar
  58. 58.
    Fernandez F, Tomassini M, Vanneschni L (2003b) Saving computational effort in genetic programming by means of plagues. In: Congress on evolutionary computation (CEC’2003). IEEE Press, New York, pp 2042–2049Google Scholar
  59. 59.
    Fernandez F, Tomassini M, Vanneschi L (2003c) An empirical study of multipopulation genetic programming. Genetic Program Evolvable Mach 4(1): 21–51MATHCrossRefGoogle Scholar
  60. 60.
    Fernandez F, Martin A (2004a) Saving effort in parallel GP by means of plagues. In: Proceedings of genetic programming. Lecture notes in computer science, pp 269–278Google Scholar
  61. 61.
    Fernandez T (2004) Virtual ramping of genetic programming populations. In: Proceedings of genetic and evolutionary computation GECCO 2004, PT 2. Lecture notes in computer science, pp 471–482Google Scholar
  62. 62.
    Fernandez F, Tomassini M, Vanneschni Land Cuendet J (2004a) A new technique for dynamic size populations in genetic programming. Congress on evolutionary computation (CEC’2004). IEEE, New York, pp 486–493Google Scholar
  63. 63.
    Fillon C, Bartoli A (2006) A divide & conquer strategy for improving efficiency and probability of success in genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 13–23Google Scholar
  64. 64.
    Fogelberg C, Zhang M (2005) Linear Genetic Programming for Multi-class Object Classification. In: Zhang S, Jarvis R (eds) Proceedings of AI 2005: advances in artificial intelligence, 18th Australian Joint conference on artificial intelligence, vol 3809, pp 369–379Google Scholar
  65. 65.
    Folino G, Pizzuti C, Spezzano G (2003a) A scalable cellular implementation of parallel genetic programming. IEEE Trans Evolut Comput 7(1): 37–53CrossRefGoogle Scholar
  66. 66.
    Folino G, Pizzuti C, Spezzano G, Vanneschi L, Tomassini M (2003b) Diversity analysis in cellular and multipopulation genetic programming. In: The 2003 congress on evolutionary computation, CEC ’03, vol 1, pp 305–311Google Scholar
  67. 67.
    Folino G, Spezzano G (2006) P-CAGE: an environment for evolutionary computation in peer-to-peer systems. In: Proceedings of genetic programming. Lecture notes in computer science, pp 341–350Google Scholar
  68. 68.
    Freitag MN, Hopper NJ (1999) AppGP: an alternative structural representation for GP. In: Proceedings of the 1999 congress on evolutionary computation, CEC 99, vol 2, pp 1377–1383Google Scholar
  69. 69.
    Fry R, Tyrrell A (2003) Enhancing the performance of GP using an ancestry-based mate selection scheme. In: Proceedings of genetic and evolutionary computation GECCO 2003, pt 2. Lecture notes in computer science, pp 1804–1805Google Scholar
  70. 70.
    Fuchs M (1999) Large Populations are not always the best choice in genetic programming. In: Proceedings of the genetic and evolutionary computation conference GECCO, pp 1033–1038Google Scholar
  71. 71.
    Fukunaga AS, Kahng AB (1995) improving the performance of evolutionary optimization by dynamically scaling the evaluation function. Evolut Comput 1: 182–187Google Scholar
  72. 72.
    Gagne C, Schoenauer M, Parizeau M, Tomassini M (2006) Genetic programming, validation sets, and parsimony pressure. In: Proceedings of genetic programming. Lecture notes in computer science, pp 109–120Google Scholar
  73. 73.
    Galeano G, Fernandez F, Tomassini M, Vanneschi L (2002) Studying the influence of synchronous and asynchronous parallel GP on programs length evolution. In: Proceedings of the congress on evolutionary computation, CEC ’02, vol 2, pp 1727–1732Google Scholar
  74. 74.
    Garcia S, Levine J, Gonzalez F (2003) Multi niche parallel GP with a junk-code migration model. In: Proceedings of genetic programming. Lecture notes in computer science, pp 327–334Google Scholar
  75. 75.
    Garcia-Arnau M, Manrique D, Rios J, Rodriguez-Paton A (2007) Initialization method for grammar-guided genetic programming. Knowl Based Syst 20(2): 127–133CrossRefGoogle Scholar
  76. 76.
    Gelly S, Teytaud O, Bredeche N, Schoenauer M (2005) A statistical learning theory approach of bloat. In: GECCO ’05: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp 1783–1784Google Scholar
  77. 77.
    Guo H, Nandi AK (2006) Breast cancer diagnosis using genetic programming generated feature. Pattern Recogn 39(5): 980–987CrossRefGoogle Scholar
  78. 78.
    Gusikhin O, Rychtyckyj N, Filev D (2007) Intelligent systems in the automotive industry: applications and trends. Knowl Inf Syst 12(2): 147–168CrossRefGoogle Scholar
  79. 79.
    Gustafon SM, Hsu WH (2001) Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem. In: Miller JF et al (eds) Proceedings of EuroGP 2001. Lecture notes in computer science, vol 2038. Springer, Heidelberg, pp 291–301Google Scholar
  80. 80.
    Gustafson S, Burke EK (2006) The speciating island model: an alternative parallel evolutionary algorithm. J Parallel Distrib Comput 66(8): 1025–1036MATHCrossRefGoogle Scholar
  81. 81.
    Hao HT, Hoai NX, McKay RB (2004) Does this matter where to start in grammar guided genetic programming? In: Proceedings of the second Pacific Asian Workshop in Genetic Programming, Cairns, AustraliaGoogle Scholar
  82. 82.
    Handley S (1994) On the use of a directed acyclic graph to represent a population of computer programs. In: Proceedings of the IEEE conference on evolutionary computation, pp 154–159Google Scholar
  83. 83.
    Harper R, Blair A (2005) A structure preserving crossover in grammatical evolution. In: IEEE congress on evolutionary computation, pp 2537–2544Google Scholar
  84. 84.
    Harper R, Blair A (2006a) A self-selecting crossover operator. In: IEEE Congress on evolutionary computation, CEC 2006, pp 1420–1427Google Scholar
  85. 85.
    Harper R, Blair A (2006b) Dynamically defined functions in grammatical evolution. In: IEEE congress on evolutionary computation, CEC 2006, pp 2638–2645Google Scholar
  86. 86.
    Heywood MJ, Zincir-Heywood AN (2000) Page-based linear genetic programming. In: IEEE international conference on systems, man, and cybernetics, pp 3823–3828Google Scholar
  87. 87.
    Heywood MI, Zincir-Heywood AN (2002) Dynamic page based crossover in linear genetic programming. IEEE Trans Syst Man Cybern B Cybern 32(3): 380–388CrossRefGoogle Scholar
  88. 88.
    Hinchliffe M, Hiden H, McKay B, Willis M, Tham M, Barton G (1996) Modelling chemical process systems using a multi-gene genetic programming algorithm. In: Koza (ed) Late Breaking papers at the genetic programming 1996 conference, pp 56–65Google Scholar
  89. 89.
    Hirasawa K, Okubo M, Katagiri H, Hu J, Murata J (2006) Comparison between genetic network programming (GNP) and genetic programming (GP). In: Proceedings of the 2001 congress on evolutionary computation, vol 2, pp 1276–1282Google Scholar
  90. 90.
    Hoai NX, McKay RI (2001) A framework for tree adjunct grammar guided genetic programming. In: Proceedings of the post-graduate ADFA conference on computer science (PACCS 01), pp 93–99Google Scholar
  91. 91.
    Hoai NX, McKay RI, Abbass HA (2003) Tree adjoining grammars, language bias, and genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 335–344Google Scholar
  92. 92.
    Hondo N, Iba H, Kakazu Y (1996) Sharing and refinement for reusable subroutines of genetic programming. In: Proceedings of IEEE international conference on evolutionary computation, pp 565–570Google Scholar
  93. 93.
    Howard LM, D’Angelo DJ (1995) The GA-P: a genetic algorithm and genetic programming hybrid. Can J Fish Aquat Sci 10(3): 11–15Google Scholar
  94. 94.
    Hsu WH, Gustafon SM (2002) Genetic programming and multi-agent layered learning by reinforcements. In: Proceedings of GECCO 2002, pp 764–771Google Scholar
  95. 95.
    Hsu WH, Harmon SJ, Rodriguez E, Zhong C (2004) Empirical comparison of incremental reuse strategies in genetic programming for keep-away soccer. In: GECCO 2004, late-breaking papersGoogle Scholar
  96. 96.
    Iba H, deGaris H, Sato T (1994) Genetic programming using a minimum description length principle. In: Kinnear KE (eds) Advances in genetic programming. MIT Press, CambridgeGoogle Scholar
  97. 97.
    Iba H, deGaris H, Sato T (1995a) Temporal data processing using genetic programming. In: Proceedings of 6th international conference on genetic algorithms, pp 279–286Google Scholar
  98. 98.
    Iba H, Sato T, deGaris H (1995b) Recombination guidance for numerical genetic programming. In: IEEE international conference on evolutionary computation, vol 1, pp 97–102Google Scholar
  99. 99.
    Iba H, de Garis H (1996) Extending genetic programming with recombinative guidance. In: Angeline PJ, Kinnear KE (eds) Advances in genetic programming, vol 2, Chap 4. MIT Press, Cambridge, pp 69–88Google Scholar
  100. 100.
    Iba H (1996) Random tree generation for genetic programming, source Lecture Notes In Computer Science, vol 1141. In: Proceedings of the 4th international conference on parallel problem solving from nature, pp 144–153Google Scholar
  101. 101.
    Imae J, Kikuchi Y, Ohtsuki N, Kobayashi T, Guisheng Zhai (2004) Design of nonlinear control systems by means of differential genetic programming. In: 43rd IEEE conference on decision and control, CDC, vol 3, pp 2734–2739Google Scholar
  102. 102.
    Ishida CY, Pozo A (2003) Grammatically based genetic programming for mining relational databases, Proceedings. 23rd International Conference of the Chilean Computer Science Society, SCCC 2003, pp 86–95Google Scholar
  103. 103.
    Ito T, Iba H, Sato S (1998a) Depth-dependent crossover for genetic programming, evolutionary computation proceedings. In: IEEE World Congress, the 1998 IEEE international conference on computational intelligence, pp 775–780Google Scholar
  104. 104.
    Ito T, Iba H, Sato S (1998b) Non-destructive depth-dependent crossover for genetic programming. In: Proceedings of the first european workshop on genetic programming. LNCS, vol 1391. Springer, Heidelberg, pp 71–82Google Scholar
  105. 105.
    Jackson D (2005) Fitness evaluation avoidance in Boolean GP problems. In: The 2005 IEEE congress on evolutionary computation, vol 3, pp 2530–2536Google Scholar
  106. 106.
    Jackson D (2005) Dormant program nodes and the efficiency of genetic programming. In: GECCO ’05: proceedings of the 2005 conference on genetic and evolutionary computation, pp 1745–1751Google Scholar
  107. 107.
    Jackson D, Gibbons AP (2007a) Layered learning in Boolean GP problems. In: Proceedings od EuroGP 2007. Lecture notes in computer science, vol 4445. Springer, Heidelberg, pp 148–159Google Scholar
  108. 108.
    Jackson D (2007b) Hierarchical genetic programming based on test input subsets. In: GECCO ’07: proceedings of the 9th annual conference on genetic and evolutionary computation, pp 1612–1619Google Scholar
  109. 109.
    Joshi AK, Levy LS, Takahashi M (1975) Tree adjunct grammars. J Comp Syst Sci 10: 136–163MATHMathSciNetCrossRefGoogle Scholar
  110. 110.
    Juillé H, Pollack JB (1996) Massively parallel genetic programming. In: Angeline PJ, Kinnear KE (eds) Advances in genetic programming, vol 2, Chap 17. MIT Press, Cambridge, pp 339–358Google Scholar
  111. 111.
    Kalganova T, Miller JF (1999) Evolving more efficient digital circuits by allowing circuit layout evolution and multiobjective fitness. In: Keymeulen AD, Lohn I (eds) Proceedings of the 1st NASMDoD workshop on evolvable hardware (EH’99). IEEE Computer Society Press, New York, pp 54–63CrossRefGoogle Scholar
  112. 112.
    Kantschik W, Banzhaf W (2001) Linear-tree GP and its comparison with other GP structures. In: Proceedings of Genetic Programming. Lecture notes in computer science, pp 302–312Google Scholar
  113. 113.
    Katagami D, Yamada S (1999) Speedup of evolutionary behavior learning with crossover depending on the usage frequency of a node. In: IEEE International conference on systems, man, and cybernetics, IEEE SMC ’99 Conference Proceedings, vol 5, pp 601–606Google Scholar
  114. 114.
    Keijzer M, Ryan C, O’Neill M, Cattolico M, Babovic V (2001) Ripple crossover in genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 74–86Google Scholar
  115. 115.
    Keijzer M (2004) Alternatives in subtree caching for genetic programming. In: Proceedings of genetic programming 7th European conference, EuroGP 2004. LNCS, vol 3003. Springer, Heidelberg, pp 328–337Google Scholar
  116. 116.
    Kennedy CJ, Giraud-Carrier C (1999) A depth controlling strategy for strongly typed evolutionary programming. In: Banzhaf W, Daida J, Eiben E et al (eds) GECCO-1999. Morgan Kaufman, Menlo Park, pp 1–6Google Scholar
  117. 117.
    Kessler M, Haynes T (1999) Depth-fair crossover in genetic programming. In: SAC ’99: Proceedings of the 1999 ACM symposium on applied computing, pp 319–323Google Scholar
  118. 118.
    Kim JJ, Zhang BT (1999) Effects of selection schemes in genetic programming for time series prediction. In: Proceedings of the 1999 congress on evolutionary computation, CEC 99, vol 1, pp 252–258Google Scholar
  119. 119.
    Kim M, Hiroyasu T, Miki M (2004) SPEA2+: improving the performance of the strength pareto evolutionary algorithm2, parallel problem solving from nature—PPSN VIII, pp 742–751Google Scholar
  120. 120.
    Kinnear KE (1994) Alternatives in automatic function definition, comparison of performance in advances in genetic programming. MIT Press, Cambridge, pp 119–141Google Scholar
  121. 121.
    Korenaga M, Hagiwara M (1998) Modified genetic programming based on elastic artificial selection and improved minimum description length. In: IEEE international conference on systems, man, and cybernetics, vol 3, pp 2348–2353Google Scholar
  122. 122.
    Kouchakpour P, Zaknich A, Bräunl T (2007) Population Variation in Genetic Programming. Inf Sci 177(17): 3438–3452CrossRefGoogle Scholar
  123. 123.
    Kouchakpour P, Zaknich A, Bräunl T (2008) Dynamic population variation in genetic programming. Inf Sci (to be printed)Google Scholar
  124. 124.
    Koza JR (1989) Hierarchical genetic algorithms operating on populations of computer programs. In: The 11th international conference on genetic algorithms, ICGA, pp 768–774Google Scholar
  125. 125.
    Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT, CambridgeMATHGoogle Scholar
  126. 126.
    Koza JR, Keane MA, Rice JP (1993) Performance improvement of machine learning via automatic discovery of facilitating functions as applied to a problem of symbolic system identification, neural networks. In: IEEE international conference, vol 1, pp 191–198Google Scholar
  127. 127.
    Koza JR (1994) Genetic programming II, automatic discovery of reusable programs. MIT Press, CambridgeMATHGoogle Scholar
  128. 128.
    Koza JR (1995) Two ways of discovering the size and shape of a computer program to solve a problem. In: ICGA, 1995. Morgan Kaufmann, Menlo Park, pp 287–294Google Scholar
  129. 129.
    Koza JR, Bennett FH, Andre D (1999) Genetic programming III. Darwinian Invention and Problem Solving. Morgan Kaufmann, San FranciscoGoogle Scholar
  130. 130.
    Koza JR, Keane MA, Streeter MJ et al (2005) Genetic programming IV: routine human-competitive machine intelligence. Kluwer, NorwellGoogle Scholar
  131. 131.
    Kramer MD, Zhang D (2000) GAPS: a geneticprogramming system. In: The 24th annual international computer software and applications conference, COMPSAC 2000, pp 614–619Google Scholar
  132. 132.
    Lang KJ (1995) Hill climbing beats genetic search on a Boolean circuit synthesis of Koza’s. In: Proceedings of the 12th international conference on machine learning, pp 340–343Google Scholar
  133. 133.
    Langdon WB, Poli R (1998) Fitness causes bloat: mutation. In: 1st European Workshop on genetic programming. Springer, Heidelberg, pp 37–48Google Scholar
  134. 134.
    Langdon WB (1998) The evolution of size in variable length representations. In: ICEC’98. IEEE Press, New York, pp 633–638Google Scholar
  135. 135.
    Langdon WB (1999) Size fair and homologous tree genetic programming crossovers. In: Proceedings genetic and evolutionary computation conference, GECCO-99, Washington DC, pp 1092–1097Google Scholar
  136. 136.
    Langdon WB, Soule T, Poli R, Foster JA (1999) The evolution of size and shape. In: Spector L, Langdon WB, O’Reilly U-M, Angeline P, (ed). Advances in genetic programming III. MIT Press, Cambridge, pp 163–190Google Scholar
  137. 137.
    Langdon WB (2000a) Quadratic bloat in genetic programming. In: GECCO, 2000. Morgan Kaufmann, Cambrdige, pp 451–458Google Scholar
  138. 138.
    Langdon WB (2000b) Size fair and homologous tree genetic programming crossovers. Genetic Program Evolv Mach 1(1/2): 95–119MATHCrossRefGoogle Scholar
  139. 139.
    Lasarczyk CWG, Dittrich P, Banzhaf W (2004) Dynamic subset selection based on a fitness case topology. Evolut Comput 12(2): 223–242CrossRefGoogle Scholar
  140. 140.
    Lee CY, Yao X (2004) Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Trans Evolut Comput 8(1): 1–13CrossRefGoogle Scholar
  141. 141.
    Leung KS, Lee KH, Cheang SM (2003) Parallel programs are more evolvable than sequential programs. In: Proceedings of Genetic programming. Lecture notes in computer science, pp 107–118Google Scholar
  142. 142.
    Lin JY, Ke HR, Chien BC, Yang WP (2007) Designing a classifier by a layered multi-population genetic programming approach. Pattern Recogn 40(8): 2211–2225MATHCrossRefGoogle Scholar
  143. 143.
    Lin WY, Kuo IC (2004) A genetic selection algorithm for OLAP data cubes. Knowl Inf Syst 6(1): 83–102CrossRefGoogle Scholar
  144. 144.
    Lones M A, Tyrrell AM (2001) Enzyme genetic programming. In: Proceedings of the 2001 congress on evolutionary computation, vol 2, pp 1183–1190Google Scholar
  145. 145.
    Luke S (2000) Two fast tree-creation algorithms for genetic programming. IEEE Trans Evolut Comput 4(3): 274–283CrossRefGoogle Scholar
  146. 146.
    Luke S, Panait L (2002a) Fighting bloat with nonparametric parsimony pressure, parallel problem solving from nature—PPSN VII, Lecture notes in computer science, vol 2439. Springer, Heidelberg, p 411Google Scholar
  147. 147.
    Luke S, Panait L (2002b) Lexicographic parsimony pressure. In: GECCO 2002, Proceedings of the Genetic and evolutionary computation conference. Springer, Heidelberg, pp 829–836Google Scholar
  148. 148.
    Luke S (2003) Modification point depth and genome growth in genetic programming. Evolut Comput 11(1): 67–106CrossRefGoogle Scholar
  149. 149.
    Luke S, Balan GC, Panait L (2003) Population implosion in genetic programming. In: Proceedings of genetic and evolutionary computation GECCO 2003, PT II. Lecture notes in computer science, pp 1729–1739Google Scholar
  150. 150.
    Luke S, Partait L (2006) A comparison of bloat control methods for genetic programming. Evolut Comput 14(3): 309–344CrossRefGoogle Scholar
  151. 151.
    Majeed H, Ryan C (2006a) A less destructive, context-aware crossover operator for GP. In: Collet P et al (eds) Proceedings of EuroGP 2006. LNCS, vol 3905. Springer, Heidelberg, pp 36–48Google Scholar
  152. 152.
    Majeed H, Ryan C (2006b) Using context-aware crossover to improve the performance of GP. In: GECCO ’06: proceedings of the 8th annual conference on Genetic and evolutionary computation, pp 847–854Google Scholar
  153. 153.
    Majeed H, Ryan C (2007) On the constructiveness of context-aware crossover. In: GECCO ’07: proceedings of the 9th annual conference on genetic and evolutionary computation, pp 1659–1666Google Scholar
  154. 154.
    Manrique D, Marquez F, Rios J, Rodriguez-Paton A (2005) Grammar based crossover operator in genetic programming. In: Proceedings of artificial intelligence and knowledge engineering applications: a bioinspired approach, PT 2. Lecture notes in computer science, pp 252–261Google Scholar
  155. 155.
    Manrique D, Ríos J, Rodríguez-Patón A (2006) Evolutionary system for automatically constructing and adapting radial basis function networks. Int J Neuro-computation, pp 2268–2283Google Scholar
  156. 156.
    McPhee NF, Miller JD (1995) Accurate replication in genetic programming. In: Eshelman L (ed) Genetic algorithms: proceedings of the sixth international conference (ICGA95). Morgan Kaufmann, Menlo Park, pp 303–309Google Scholar
  157. 157.
    McPhee NF, Jarvis A, Crane EF (2004) On the strength of size limits in linear genetic programming. In: Proceedings of genetic and evolutionary computation GECCO 2004 , PT 2. Lecture notes in computer science, pp 593–604Google Scholar
  158. 158.
    McKay R, Abbass HA (2001) Anti-correlation: a diversity promoting mechanisms in ensemble learning, Austr J Intell Inf Process Syst (3/4) 7:139–149Google Scholar
  159. 159.
    Miller JF, Thomson P (2000) Cartesian genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 121–132Google Scholar
  160. 160.
    Miller JF, Smith SL (2006) Redundancy and computational efficiency in Cartesian genetic programming. IEEE Trans Evolut Comput 10(2): 167–174CrossRefGoogle Scholar
  161. 161.
    Monsieurs P, Flerackers E (2001) Reducing bloat in genetic programming. In: Proceedings of computational intelligence: theory and applications. Lecture notes in computer science, pp 471–478Google Scholar
  162. 162.
    Moore FW, Garcia ON (1997) A new methodology for reducing brittleness in genetic programming. In: Proceedings of the IEEE, aerospace and electronics conference, NAECON, vol 2, pp 757–763Google Scholar
  163. 163.
    Muntean O, Diosan L, Oltean M (2007) Best SubTree genetic programming. In: GECCO ’07: proceedings of the 9th annual conference on genetic and evolutionary computation, pp 1667–1673Google Scholar
  164. 164.
    Nanduri DT, Ciesielski V (2005) Comparison of the effectiveness of decimation and automatically defined functions. In: Proceedings of knowledge-based intelligent information and engineering systems, PT 3. Lecture notes in artificial intelligence, pp 540–546Google Scholar
  165. 165.
    Nguyen XH, McKay RI, Essam D (2006) Representation and structural difficulty in genetic programming. IEEE Tran Evolut Comput 10(2): 157–166CrossRefGoogle Scholar
  166. 166.
    Niehaus J, Igel C, Banzhaf W (2007) Reducing the number of fitness evaluations in graph genetic programming using a canonical graph indexed database. Evolut Comput 15(2): 199–221CrossRefGoogle Scholar
  167. 167.
    Niehaus J, Banzhaf W (2001) Adaption of operator probabilities in genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 325–336Google Scholar
  168. 168.
    Niimi A, Tazaki E (1999) Extended genetic programming using reinforcement learning operation. In: Proceedings of IEEE international conference on systems, man, and cybernetics, SMC ’99 conference, vol 5, pp 596–600Google Scholar
  169. 169.
    Niwa T, Iba H (1996) Distributed genetic programming—empirical study and analysis, In: Koza JR, Goldberg D, Fogel DB, Riolo RL (eds) Genetic programming 1996: proceedings of the first annual conference, 38–31 July, Stanford University. MIT Press, Cambridge, pp 339–344Google Scholar
  170. 170.
    Nordin JP (1994) Genetic programming and emergent intelligence. In: Kinnear KE (eds) Advances in genetic programming, vol 1, Chap 14. MIT Press, Cambridge, pp 311–331Google Scholar
  171. 171.
    Nordin P, Banzhaf W (1994) Complexity compression and evolution. In: Eshelman L (eds) Genetic algorithms: proceedings of the sixth international conference (ICGA95). Morgan Kaufmann, Menlo Park, pp 310–317Google Scholar
  172. 172.
    Nordin P, Francone F, Banzhaf W (1996) Explicitly defined introns and destructive crossover in genetic programming. Adv Genetic Program 2: 111–134Google Scholar
  173. 173.
    Nordin JP, Banzhaf W, Francone FD (1999) A compiling genetic programming systems that directly manipulates the machine code. In: Spector L, Langdon WB, O’Reilly U-M, Angeline PJ (eds) Advances in genetic programming, vol 3, Chap 12. MIT Press, Cambridge, pp 275–299Google Scholar
  174. 174.
    Nordin P, Hoffmann F, Francone FD, Brameier M, Banzhaf W (1999) AIM-GP and parallelism. In: Proceedings of the 1999 congress on evolutionary computation, CEC 99, vol 2, pp 1059–1066Google Scholar
  175. 175.
    Oltean M (2004) Solving even-parity problems using traceless genetic programming. In: Congress on evolutionary computation, CEC2004, vol 2, pp 1813–1819Google Scholar
  176. 176.
    O’Reilly UM, Oppacher F (1995) Hybridized crossover-based search techniques for program discovery. In: IEEE international conference on evolutionary computation, vol 2, pp 573–578Google Scholar
  177. 177.
    Oussaidène M, Chopard B, Pictet O V, Tomassini M (1996) Parallel genetic programming: an application to trading models evolution. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic programming 1996: proceedings of the first annual conference. MIT Press, Cambridge, pp 357–380Google Scholar
  178. 178.
    Page J, Poli R, Langdon WB (1999) Smooth uniform crossover with smooth point mutation in genetic programming: a preliminary study. In: Proceedings of genetic programming. Lecture notes in computer science, pp 39–48Google Scholar
  179. 179.
    Panait L, Luke S (2004) Alternative bloat control methods. In: Proceedings of genetic and evolutionary computation GECCO 2004, PT 2. Lecture notes in computer science, pp 630–641Google Scholar
  180. 180.
    Parent J, Nowe A, Steenhaut K, Defaweux A (2005) Linear genetic programming using a compressed genotype representation. In: The IEEE congress on evolutionary computation, vol 2, pp 1164–1171Google Scholar
  181. 181.
    Perry JE (1994) The effect of population enrichment in genetic programming. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE World Congress on Computational Intelligence, vol 1, pp 456–461Google Scholar
  182. 182.
    Piszcz A, Soule T (2006) Genetic programming: optimal population sizes for varying complexity problems. In: GECCO ’06: proceedings of the 8th annual conference on genetic and evolutionary computation, pp 953–954Google Scholar
  183. 183.
    Platel MD, Clergue M, Collard P (2006) Size control with maximum homologous crossover. Artificial Evolution Lecture notes in computer science, pp 13–24Google Scholar
  184. 184.
    Poli R, Langdon WB (1997) A new schema theory for genetic programming with one-point crossover and point mutation. 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 278–285Google Scholar
  185. 185.
    Poli R, Langdon WB (1998a) Schema theory for genetic programming with one-point crossover and point mutation. Evolut Comput 6(3): 231–252CrossRefGoogle Scholar
  186. 186.
    Poli R and Langdon WB (1998b) A review of theoretical and experimental results on schemata in genetic programming. In: Banzhaf W et al (eds) Proceedings of the first European workshop on genetic programming, vol 1391, pp 1–15Google Scholar
  187. 187.
    Poli R and Langdon WB (1998c) On the search properties of different crossover operators in genetic programming. In: Genetic programming 1998: proceedings of the third annual conference. Morgan Kaufmann, Menlo Park, pp 293–301Google Scholar
  188. 188.
    Poli R (2003) A simple but theoretically-motivated method to control bloat in genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 204–217Google Scholar
  189. 189.
    Poli R (2005) Tournament selection, iterated coupon-collection problem, and backward-chaining evolutionary algorithms. In: Alden H et al, Schmitt (eds) Foundations of genetic algorithms: 8th international workshop (FOGA). Lecture notes in computer science, vol~3469, pp~132–155Google Scholar
  190. 190.
    Punch WF, Zongker D, Goodman ED (1996) The royal tree problem, a benchmark for single and multi-population genetic programming. In: Angeline PJ, Kinnear KE (eds) Advances in genetic programming, vol 2, Chap 15. The MIT Press, Cambridge, pp 299–316Google Scholar
  191. 191.
    Punch WF (1998) How effective are multiple populations in genetic programming. In: Koza JR, Banzhaf W, Chellapilla K, Deb K, Dorigo M, Fogel DB, Garzon MH, Goldberg DE, Iba H, Riolo R (eds) Proceedings of the third annual conference on genetic programming.. Morgan Kaufmann, San Mateo, pp 308–313Google Scholar
  192. 192.
    Ratle A, Sebag M (2001) Avoiding the bloat with probabilistic grammar-guided genetic programming. In: Collet P, Fonlupt C, Hao J-K, Lutton E, Schoenauer M (eds) Artificial evolution 5th International conference, Evolution Artificielle, EA 2001, vol 2310.. Springer, Heidelberg, pp 255–266Google Scholar
  193. 193.
    Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH (2003) Optimization of neural network architecture using genetic programming improves detection of gene–gene interactions in studies of human diseases. BMC Bioinf 4:28Google Scholar
  194. 194.
    Ritchie MD, Coffey CS, Moore JH (2004) Genetic programming neural networks as a bioinformatics tool for human genetics. In: Proceedings of genetic and evolutionary computation—GECCO 2004, PT 1. Lecture notes in computer science, Part 1, pp 438–448Google Scholar
  195. 195.
    Roberts SC, Howard D, Koza JR (2001) Evolving modules in genetic programming by subtree encapsulation. In: Proceedings of genetic programming. Lecture notes in computer science, pp 160–175Google Scholar
  196. 196.
    Rochat D, Tomassini M, Vanneschi L (2005) Dynamic size populations in distributed genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 50–61Google Scholar
  197. 197.
    Rodriguez-Vazquez K, Fonseca CM, Fleming PJ (2004) Identifying the structure of non-linear dynamic systems using multiobjective genetic programming. IEEE Trans Syst Man Cybern A Syst Humans, pp 531–547Google Scholar
  198. 198.
    Rodriguez-Vazquez K, Fleming PJ (2005) Evolution of mathematical models of chaotic systems based on multiobjective genetic programming. Knowl Inf Syst 8(2): 235–256CrossRefGoogle Scholar
  199. 199.
    Rosca JP, Ballard DH (1994a) Hierarchical self-organization in genetic programming, machine learning. In: Proceeding of the 11th international conference on machine learning, pp 25 1–258Google Scholar
  200. 200.
    Rosca JP, Ballard DH (1994b) Genetic programming with adaptive representations. Technical Report TR 489, University of Rochester, Computer Science Department, Rochester, NY, USA, pp 1–30Google Scholar
  201. 201.
    Rosca JP, Ballard DH (1996) Discovery of Subroutines in Genetic Programming. In: Angeline P, Kinnear KE (eds) Proceedings of advances in genetic programming, vol 2, Chap 9. MIT Press, Cambridge, pp 177–202Google Scholar
  202. 202.
    Rosca JP (1997) Analysis of complexity drift in genetic programming, in Genetic Programming 1997. In: Koza JR, Deb K, Dorigo M, Fogel DB, Garzon M, Iba H, Riolo RL (eds) Proceedings of the second annual conference. Morgan Kaufmann, pp~286–294Google Scholar
  203. 203.
    Ryan C, Collins JJ, O’Neill M (1998) Grammatical evolution: Evolving programs for an arbitrary language. In: Banzhaf W et al (eds) 1st European workshop on genetic programming. Lecture notes in computer science, vol 1391. SpringerGoogle Scholar
  204. 204.
    Salhi A, Glaser H, De Roure D (1998) Parallel implementation of a genetic-programming based tool for symbolic regression. Inform Process Lett 66(6): 299–307CrossRefGoogle Scholar
  205. 205.
    Sanchez L (2000) Interval-valued GA-P algorithms. IEEE Trans Evolut Comput 4(1): 64–72CrossRefGoogle Scholar
  206. 206.
    Silva S, Almeida JS (2003) Dynamic maximum tree depth - a simple technique for avoiding bloat in tree-based GP. In: Cantu-Paz E, Foster JA, Deb K et al (eds) GECCO-2003. LNCS, ChicagoGoogle Scholar
  207. 207.
    Silva S, Costa E (2004) Dynamic limits for bloat control-variations on size and depth. In: Deb K, Poli R, Banzhaf W et al (eds) GECCO-2004, Seattle, WA, USA. LNCS. Springer, Heidelberg, pp 666–677Google Scholar
  208. 208.
    Silva S, Silva PJN (2005) Costa E. Resource-limited genetic programming: Replacing tree depth limits. In: Ribeiro B, Albrecht RF, Dobnikar A et al (eds) ICANNGA-2005, Coimbra, Portugal. Springer, Heidelberg, pp 243–246Google Scholar
  209. 209.
    Silva S, Costa E (2005a) Resource-limited genetic programming: the dynamic approach, genetic and evolutionary computation conference. In: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp 1673–1680Google Scholar
  210. 210.
    Silva S, Costa E (2005b) Comparing tree depth limits and resource-limited GP. In: The 2005 IEEE congress on evolutionary computation, vol 1, pp 920–927Google Scholar
  211. 211.
    Soule T, Foster JA, Dickinson J (1996) Code Growth in GP, in GP. The MIT Press, Cambridge, pp 215–223Google Scholar
  212. 212.
    Soule T, Foster JA (1999) Effects of code growth and parsimony pressure on populations in genetic programming. Evolut Comput 6(4): 293–309CrossRefGoogle Scholar
  213. 213.
    Soule T (2002) Exons and code growth in genetic programming. In: Foster A, Lutton E, Miller J, Ryan C, Tettamanzi AGB (eds) EuroGP 2002 LNCS, vol 2278. Springer, Heidelberg, pp 142–151Google Scholar
  214. 214.
    Soule T, Heckendorn RB (2002) An analysis of the causes of code growth in genetic programming. Genetic Program Evol Mach 3: 283–309MATHCrossRefGoogle Scholar
  215. 215.
    Spinosa E, Pozo A (2004) Controlling the population size in genetic programming. In: Proceedings of advances in artificial intelligence. Lecture Notes in Artificial Intelligence, pp 345–354Google Scholar
  216. 216.
    Stevens J, Heckendorn RB, Soule T (2005) Exploiting disruption aversion to control code bloat. In: GECCO ’05: proceedings of the 2005 conference on Genetic and evolutionary computation, pp 1605–1612Google Scholar
  217. 217.
    Stone P, Veloso M (2000) Layered Learning. In: Proceedings of 17th international conference on machine learning. Springer, Heidelberg, pp 369–381Google Scholar
  218. 218.
    Streeter MJ (2003) The root causes of code growth in genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 443–454Google Scholar
  219. 219.
    Svangard N, Nordin P, Lloyd S (2003) Using genetic programming with negative parsimony pressure on exons for portfolio optimization. The 2003 congress on evolutionary computation, CEC ’03, vol 2, pp 1014–1017Google Scholar
  220. 220.
    Tackett WA (1994) Recombination, selection and the genetic construction of computer programs. PhD dissertation, University of Southern California. Department of Electrical Engineering SystemsGoogle Scholar
  221. 221.
    Tackett WA, Carmi A (1994) The unique implications of brood selection for genetic programming. In: IEEE World congress on computational intelligence, proceedings of the first IEEE conference on evolutionary computation, vol 1, pp 160–165Google Scholar
  222. 222.
    Tanev I, Uozumi T, Ono K (2001) Parallel genetic programming: component object-based distributed collaborative approach. In: Proceedings of 15th international conference on information networking, pp 129–136Google Scholar
  223. 223.
    Terrio MD, Heywood MI (2002) Directing crossover for reduction of bloat in GP. In: IEEE proceedings of Canadian conference on electrical and computer engineering, vol 2, pp 1111–1115Google Scholar
  224. 224.
    Tomassini M (1999) Parallel and distributed evolutionary algorithms: a review. In: Neittaanmki P, Miettinen K, Mkel M, Periaux J (eds) Evolutionary algorithms in engineering and computer science. Wiley, ChichesterGoogle Scholar
  225. 225.
    Tomassini M, Vanneschi L, Fernandez F, Galeano G (2004) A study of diversity in multipopulation genetic programming. In: Artificial evolution. Lecture notes in computer science, pp 243–255Google Scholar
  226. 226.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput, pp 67–82Google Scholar
  227. 227.
    Walker JA, Miller JF (2007) The automatic acquisition, evolution and reuse of modules in Cartesian genetic programming. IEEE Trans Evolut Comput (accepted)Google Scholar
  228. 228.
    Wagner N, Michalewicz Z (2001) Genetic programming with efficient population control for financial time series prediction. In: Goodman ED (eds) GECCO-2001 late breaking papers. San Francisco, CA, USA, pp 458–462Google Scholar
  229. 229.
    Wang G, Soule T (2004) How to choose appropriate function sets for genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 198–207Google Scholar
  230. 230.
    Whigham PA (1995a) Grammatically-based genetic programming. In: Rosca JP (ed) Proceedings of the workshop on genetic programming: from theory to real-world applications, Tahoe City, California, USA, 1995, pp 33–41Google Scholar
  231. 231.
    Whigham PA (1995b) A schema theorem for context-free grammars. In: IEEE conference on evolutionary computation, vol 1. IEEE Press, New York, pp 178–181Google Scholar
  232. 232.
    Wieczorek W, Czech ZJ (2000) Grammars in genetic programming. Control Cybern 29(4): 1019–1030MATHGoogle Scholar
  233. 233.
    Wong ML, Leung KS (1995a) Applying logic grammars to induce sub-functions in genetic programming. Evolut Comput 2: 737–740Google Scholar
  234. 234.
    Wong ML, Leung KS (1995b) Combining genetic programming and inductive logic programming using logic grammars. In: IEEE international conference on evolutionary computation, vol 2, pp 733–736Google Scholar
  235. 235.
    Wong ML, Leung KS (2000) Data mining using grammar based genetic programming and applications. Kluwer, BostonMATHGoogle Scholar
  236. 236.
    Wong P, Zhang M (2006) Algebraic simplification of GP programs during evolution. In: GECCO ’06: proceedings of the 8th annual conference on genetic and evolutionary computation, pp 927–934Google Scholar
  237. 237.
    Woodward JR (2006) Complexity and Cartesian genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 260–269Google Scholar
  238. 238.
    Wyns B, Sette S, Boullart L (2004) Self-improvement to control code growth in genetic programming. In: Artificial evolution, Lecture notes in computer science, pp 256–266Google Scholar
  239. 239.
    Wyns B, Boullart L, De Smedt PJ (2007) Limiting code growth to improve robustness in tree-based genetic programming. In: GECCO ’07: proceedings of the 9th annual conference on genetic and evolutionary computation, pp 1763Google Scholar
  240. 240.
    Xie HY (2005) Diversity control in GP with ADF for regression tasks. In: AI 2005: advances in artificial intelligence. Lecture Notes In Artificial Intelligence, pp 1253–1257Google Scholar
  241. 241.
    Xie HY, Zhang MJ, Andreae P (2006a) Population clustering in genetic programming. In: Proceedings of genetic programming. Lecture notes in computer science, pp 190–201Google Scholar
  242. 242.
    Xie HY, Zhang, Andreae P (2006b) Automatic selection pressure control in genetic programming. In: Sixth international conference on intelligent systems design and applications, ISDA ’06, vol 1, pp 435–440Google Scholar
  243. 243.
    Xie H, Zhang M, Andreae P (2007a) An analysis of constructive crossover and selection pressure in genetic programming, GECCO, pp 1739–1748Google Scholar
  244. 244.
    Xie H, Zhang M, Andreae P (2007b) Another investigation on tournament selection: modelling and visualisation. GECCO, pp 1468–1475Google Scholar
  245. 245.
    Yanagiya M (1995) Efficient genetic programming based on binary decision diagrams. IEEE Int Conf Evolut Comput 1: 234–239Google Scholar
  246. 246.
    Yuen CC (2004) Selective crossover using gene dominance as an adaptive strategy for genetic programming. Msc intelligent systems, University College, London, UKGoogle Scholar
  247. 247.
    Zhang L, Nandi AK (2007) Neutral offspring controlling operators in genetic programming. Pattern Recogn 40(10): 2696–2705MATHCrossRefGoogle Scholar
  248. 248.
    Zhang BT, Miihlenbein H (1995) Balancing accuracy and parsimony in genetic programming. Evolut Comput 3(1): 17–38CrossRefGoogle Scholar
  249. 249.
    Zhang H, Lu YN, Wang F (2003) Grammar based genetic programming using linear representations. Chin J Elect 12(1): 75–78Google Scholar
  250. 250.
    Zhang M, Gao X, Lou W (2006a) Looseness Controlled Crossover in GP for Object Recognition. In: IEEE congress on evolutionary computation, CEC 2006, pp 1285–1292Google Scholar
  251. 251.
    Zhang MJ, Wong P, Qian DP (2006b) Online program simplification in genetic programming, source. In: Proceedings of simulated evolution and learning. Lecture notes in computer science, pp 592–600Google Scholar
  252. 252.
    Zhang MJ, Gao XY, Lou WJ, Qian DP (2006c) Investigation of brood size in GP with brood recombination crossover for object recognition. In: Proceedings of PRICAI 2006: trends in artificial intelligence. Lecture Notes in Artificial Intelligence, pp 923–928Google Scholar
  253. 253.
    Zhang YQ, Chen HS (2006) Improved approach of Genetic Programming and applications for data mining. In: Advances in natural computation, pt 1. Lecture notes in computer science, pp 816–819Google Scholar
  254. 254.
    Zhang Y, Rockett PI (2005) Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detection. In: Beyer et al (eds) Genetic and evolutionary computation conference (GECCO 2005), pp 795–802Google Scholar
  255. 255.
    Zhang Y, Rockett PI (2006) Feature extraction using multi-objective genetic programming. In: Jin Y (eds) Multi-objective machine learning.. Springer, HeidelbergGoogle Scholar
  256. 256.
    Zitzler E, ThieIe L (1998) An evolutionary algorithm for multiobjective optimization: the strength pareto approach. Swiss Federal Institute of Technology (ETH) Zurich, TIK-Report, No 43Google Scholar
  257. 257.
    Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evolut Comput 3(4): 257–27CrossRefGoogle Scholar
  258. 258.
    Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the performance of the strength pareto evolutionary algorithm, technical Report 103, Computer Engineering and Communication Networks Lab (TLK). Swiss Federal Institute of Technology (ETH) ZurichGoogle Scholar
  259. 259.
    Zvada S, Vanyi B (2004) Improving grammar-based evolutionary algorithms via attributed derivation trees. In: Proceedings of genetic programming. Lecture notes in computer science, pp 208–219Google Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Peyman Kouchakpour
    • 1
  • Anthony Zaknich
    • 1
  • Thomas Bräunl
    • 1
  1. 1.School of Electrical, Electronic and Computer EngineeringUniversity of Western AustraliaNedlands, PerthAustralia

Personalised recommendations