Genetic programming - introduction, applications, theory and open issues

Handbook of Natural Computing

Volumn 2-4, Issue , 2012, Pages 709-739

  Vanneschi, Leonardo ^a   Poli, Riccardo ^b  

^a UNIVERSITY OF MILANO BICOCCA   (Italy)

^b UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC PROGRAMMING; ITERATIVE METHODS;

BASIC PRINCIPLES; CROSSOVER AND MUTATION; DARWINIAN EVOLUTION; EVOLUTIONARY APPROACH; GENETIC OPERATORS; ITERATIVE PROCESS; QUALITY CRITERIA; REAL-WORLD PROBLEM;

GENETIC ALGORITHMS;

EID: 84933046441     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-540-92910-9_24     Document Type: Chapter

References (127)

1. Altenberg L (2009) Modularity in evolution: Some lowlevel questions. In: Rasskin-Gutman D, Callebaut W (eds), Modularity: Understanding the Development and Evolution of Complex Natural Systems. MIT Press, Cambridge, MA
2. Alves da Silva AP, Abrao PJ (2002) Applications of evolutionary computation in electric power systems. In: Fogel DB et al. (eds), Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, pp. 1057-1062. IEEE Press
3. Archetti F, Messina E, Lanzeni S, Vanneschi L (2007) Genetic programming for computational pharmacokinetics in drug discovery and development. Genet Programming Evol Mach 8(4):17-26
4. Azaria Y, Sipper M (2005) GP-gammon: Genetically programming backgammon players. Genet Programming Evol Mach 6(3):283-300, Sept. Published online: 12 August 2005
5. Banzhaf W, Francone FD, Nordin P (1996) The effect of extensive use of the mutation operator on generalization in genetic programming using sparse data sets. In: Ebeling W et al., (ed) 4th International Conference on Parallel Problem Solving from Nature (PPSN96), Springer, Berlin, pp. 300-309
6. Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic Programming, An Introduction. Morgan Kaufmann, San Francisco, CA
7. Barrett SJ, Langdon WB (2006) Advances in the application of machine learning techniques in drug discovery, design and development. In: Tiwari A et al. (eds), Applications of Soft Computing: Recent Trends, Advances in Soft Computing, On the World Wide Web, 19 Sept.-7 Oct. 2005. Springer, Berlin, 99-110
8. Bojarczuk CC, Lopes HS, Freitas AA (July-Aug. 2008) Genetic programming for knowledge discovery in chest-pain diagnosis. IEEE Eng Med Biol Mag 19(4):38-44
9. Brameier M, Banzhaf W (2001) A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans Evol Comput 5(1):17-26
10. Cagnoni S, Rivero D, Vanneschi L (2005) A purely-evolutionary memetic algorithm as a first step towards symbiotic coevolution. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC'05), Edinburgh, Scotland, 2005. IEEE Press, Piscataway, NJ. pp. 1156-1163
11. Castillo F, Kordon A, Smits G (2006) Robust pareto front genetic programming parameter selection based on design of experiments and industrial data. In: Riolo RL, et al. (ed) Genetic Programming Theory and Practice IV, vol 5 of Genetic and Evolutionary Computation, chapter 2. Springer, Ann Arbor, 11-13 May
12. Chen S-H, Liao C-C (2005) Agent-based computational modeling of the stock price-volume relation. Inf Sci 170(1):75-100, 18 Feb
13. Chen S-H, Wang H-S, Zhang B-T (1999) Forecasting high-frequency financial time series with evolutionary neural trees: The case of Hang Seng stock index. In: Arabnia HR, (ed), Proceedings of the International Conference on Artificial Intelligence, IC-AI '99, vol 2, Las Vegas, NV, 28 June-1 July. CSREA Press pp. 437-443
14. Da Costa LE, Landry JA (2006) Relaxed genetic programming. In: Keijzer M et al., editor, GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, vol 1, Seattle, Washington, DC, 8-12 July. ACM Press pp. 937-938
15. Dassau E, Grosman B, Lewin DR (2006) Modeling and temperature control of rapid thermal processing. Comput Chem Eng 30(4):686-697, 15 Feb
16. Davis TE, Principe JC (1993) A Markov chain framework for the simple genetic algorithm. Evol Comput 1(3):269-288
17. De Jong KA (1988) Learning with genetic algorithms: An overview. Mach Learn 3:121-138
18. Dempsey I (2007) Grammatical evolution in dynamic environments. Ph. D. thesis, University College Dublin, Ireland
19. 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: Thierens, D et al. (eds), GECCO '07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, vol 2 London, 7-11 July 2007. ACM Press, pp. 1588-1595
20. Domingos P (1999) The role ofOccam's razor in knowledge discovery. Data Mining Knowl Discov 3(4):409-425
21. Eiben AE, Jelasity M (2002) A critical note on experimental research methodology in EC. In: Congress on Evolutionary Computation (CEC'02), Honolulu, HI, 2002. IEEE Press, Piscataway, NJ, pp. 582-587
22. Esparcia-Alcazar AI, Sharman KC (Sept. 1996) Genetic programming techniques that evolve recurrent neural networks architectures for signal processing. In:IEEEWorkshop on Neural Networks for Signal Processing, Seiko, Kyoto, Japan
23. Fernandez F, Martin A (2004) Saving effort in parallel GP by means of plagues. In: Keijzer M, et al. (eds), Genetic Programming 7th European Conference, EuroGP 2004, Proceedings, vol 3003 of LNCS, Coimbra, Portugal, 5-7 Apr. Springer-Verlag, pp. 269-278
24. Fernandez F, Tomassini M, Vanneschi L (2003) Saving computational effort in genetic programming by means of plagues. In: Sarker, R et al. (eds), Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, Camberra, 8-12 Dec. 2003. IEEE Press, pp. 2042-2049
25. Fernandez F, Vanneschi L, Tomassini M (2003) The effect of plagues in genetic programming: A study of variable-size populations. In: Ryan, C et al. (ed) Genetic Programming, Proceedings of EuroGP'2003, vol 2610 of LNCS, Essex, 14-16 Apr. Springer-Verlag, pp. 317-326
26. Forrest S, MitchellM (1993) What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation. Mach Learn 13:285-319
27. Francone FD, Nordin P, Banzhaf W (1996) Benchmarking the generalization capabilities of a compiling genetic programming system using sparse data sets. In: Koza JR et al. (ed), Genetic Programming: Proceedings of the First Annual Conference, MIT Press, Cambridge, pp. 72-80
28. Fukunaga A, Stechert A (1998) Evolving nonlinear predictive models for lossless image compression with genetic programming. In: Koza, JR et al. (eds), Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, WI, 22-25 July, Morgan Kaufmann pp. 95-102
29. Gagné C, Schoenauer M, Parizeau M, Tomassini M (2006) Genetic programming, validation sets, and parsimony pressure. In: Collet P et al. (ed), Genetic Programming, 9th European Conference, EuroGP2006, Lecture Notes in Computer Science, LNCS 3905, pp. 109-120. Springer, Berlin, Heidelberg, New York
30. Giacobini M, Tomassini M, Vanneschi L (2002) Limiting the number of fitness cases in genetic programming using statistics. In: Merelo JJ, et al. (eds), Parallel Problem Solving from Nature-PPSN VII, vol 2439 of Lecture Notes in Computer Science, Springer-Verlag, Heidelberg, pp. 371-380
31. Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston, MA
32. Gustafson S, Vanneschi L (2005) Operator-based distance for genetic programming: Subtree crossover distance. In: Keijzer, M., et al. (ed), Genetic Programming, 8th European Conference, EuroGP2005, Lecture Notes in Computer Science, LNCS 3447, pp. 178-189. Springer, Berlin, Heidelberg, New York
33. Gustafson S, Vanneschi L (2008) Operator-based tree distance in genetic programming. IEEE Trans Evol Comput 12:4
34. Hansen JV, Lowry PB, Meservy RD, McDonald DM (Aug. 2007) Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection. Decis Support Syst 43(4):1362-1374, Special Issue Clusters
35. Hasan S, Daugelat S, Rao PSS, Schreiber M (June 2006) Prioritizing genomic drug targets in pathogens:Application to mycobacterium tuberculosis. PLoS Comput Biol 2(6):e61
36. Hauptman A, Sipper M (2007) Evolution of an efficient search algorithm for the mate-in-N problem in chess. In: Ebner, M et al. (eds), Proceedings of the 10th European Conference on Genetic Programming, vol 4445 of Lecture Notes in Computer Science Valencia, Spain, 11-13 Apr. Springer pp. 78-89
37. Hemberg E, Gilligan C, O'Neill M, Brabazon A (2007) A grammatical genetic programming approach to modularity in genetic algorithms. In: Ebner, M et al. (eds), Proceedings of the 10th European Conference on Genetic Programming, vol 4445 of Lecture Notes in Computer Science, Valencia, Spain, 11-13 Apr. Springer pp. 1-11
38. Holland JH (1975) Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, MI
39. Horn J, Goldberg DE (1995) Genetic algorithm difficulty and the modality of the fitness landscapes. In: Whitley D, Vose M (eds), Foundations of Genetic Algorithms, vol. 3, Morgan Kaufmann, pp. 243-269
40. Howard D, Roberts SC (2004) Incident detection on highways. In: O'Reilly, U-M et al., (eds), Genetic Programming Theory and Practice II, chapter 16, Springer, Ann Arbor, 13-15 May pp. 263-282
41. Jacob C (May-June 2000) The art of genetic programming. IEEE Intell Syst 15(3):83-84, May-June
42. Jacob C (2001) Illustrating Evolutionary Computation with Mathematica. Morgan Kaufmann, San Francisco, CA
43. Jakobović D, Budin L (2006) Dynamic scheduling with genetic programming. In: Collet, P et al. (eds), Proceedings of the 9th European Conference on Genetic Programming, vol 3905 of Lecture Notes in Computer Science, Budapest, Hungary, 10-12 Apr. Springer pp. 73-84
44. Jin N, Tsang E (2006) Co-adaptive strategies for sequential bargaining problems with discount factors and outside options. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, 6-21 July. IEEE Press, pp. 7913-7920
45. Jones T (1995) Evolutionary algorithms, fitness landscapes and search. Ph. D. thesis, University of New Mexico, Albuquerque
46. Jonyer I, Himes A (2006) Improving modularity in genetic programming using graph-based data mining. In: Sutcliffe GCJ, Goebe RG (eds), Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, pp. 556-561, Melbourne Beach, FL, May 11-13 2006. American Association for Artificial Intelligence
47. Jordaan E, den Doelder J, Smits G (2006) Novel approach to develop structure-property relationships using genetic programming. In: Runarsson TP, et al. (eds), Parallel Problem Solving from Nature-PPSN IX, vol 4193 of LNCS, Reykjavik, Iceland, 9-13 Sept. Springer-Verlag pp. 322-331
48. Kashtan N, Noor E, Alon U (2007) Varying environments can speed up evolution. Proceedings of the National Academy of Sciences, 104(34):13711-13716, August 21
49. Kattan A, Poli R (2008) Evolutionary lossless compression with GP-Z. In Proceedings of the IEEE World Congress on Computational Intelligence, Hong Kong, 1-6 June. IEEE Keijzer M (Sept. 2004) Scaled symbolic regression. Genetic Programming and Evolvable Machines, 5(3):259-269
50. Kibria RH, Li Y (2006) Optimizing the initialization of dynamic decision heuristics in DPLL SAT solvers using genetic programming. In: Collet P, et al. (eds), Proceedings of the 9th European Conference on Genetic Programming, vol. 3905 of Lecture Notes in Computer Science, Budapest, Hungary, 10-12 Apr. Springer. pp. 331-340
51. Kinnear KE Jr (1994) Fitness landscapes and difficulty in genetic programming. In: Proceedings of the First IEEE Congress on Evolutionary Computation, IEEE Press, Piscataway, NY, pp. 142-147
52. Kirchherr W, Li M, Vitanyi P (1997) The miraculous universal distribution. Math Intell 19:7-15
53. Klappenecker A, May FU (1995) Evolving better wavelet compression sche. In: Laine, AF et al. (ed), Wavelet Applications in Signal and Image Processing III, vol 2569, San Diego, CA 9-14 July. SPIE Kordon A (Sept. 2006) Evolutionary computation at Dow Chemical. SIGEVOlution, 1(3):4-9
54. Koza J, Poli R (2003) A genetic programming tutorial. In: Burke E (ed) Introductory Tutorials in Optimization, Search and Decision Support, Chapter 8. http://www.genetic-programming.com/jkpdf/burke2003tutorial.pdf
55. Koza JR (1992a) A genetic approach to the truck backer upper problem and the inter-twined spiral problem. In Proceedings of IJCNN International Joint Conference on Neural Networks, vol IV, IEEE Press, pp. 310-318
56. Koza JR (1992b) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA
57. Koza JR (1994) Genetic Programming II. The MIT Press, Cambridge, MA
58. Koza JR, Bennett FH III, Stiffelman O (1999) Genetic programming as a Darwinian invention machine. In: Poli R, et al. (eds) Genetic Programming, Proceedings of EuroGP'99, vol 1598 of LNCS, Goteborg, Sweden, 26-27 May. Springer-Verlag pp. 93-108
59. Koza JR, Bennett FH III, Andre D, Keane MA (1999) Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann, San Francisco, CA
60. Kushchu I (2002) An evaluation of evolutionary generalization in genetic programming. Artif Intell Rev 18(1):3-14
61. Langdon WB (2003) Convergence of program fitness landscapes. In: Cantú-Paz, E., et al. (ed) Genetic and Evolutionary Computation-GECCO-2003, vol 2724 of LNCS, Springer-Verlag, Berlin, pp. 1702-1714
62. Langdon WB, Buxton BF (Sept. 2004) Genetic programming for mining DNA chip data from cancer patients. Genet Programming Evol Mach, 5(3):251-257
63. Langdon WB, Poli R (1998) Genetic programming bloat with dynamic fitness. In: Banzhaf W, et al. (eds), Proceedings of the First European Workshop on Genetic Programming, vol 1391 of LNCS, Paris, 14-15 Apr. Springer-Verlag. pp. 96-112
64. Langdon WB, Poli R (2002) Foundations of Genetic Programming. Springer-Verlag
65. Langdon WB, Poli R (2005) Evolutionary solo pong players. In: Corne, D et al. (eds), Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol 3, Edinburgh, U. K., 2-5 Sept. IEEE Press pp. 2621-2628
66. Langdon WB, Soule T, Poli R, Foster JA (June 1999) The evolution of size and shape. In Spector, L et al. (eds), Advances in Genetic Programming 3, chapter 8, pp. 163-190. MIT Press, Cambridge, MA
67. Lew TL, Spencer AB, Scarpa F, Worden K, Rutherford A, Hemez F (Nov. 2006) Identification of response surface models using genetic programming. Mech Syst Signal Process 20(8):1819-1831
68. Lewin DR, Lachman-Shalem S, Grosman B (July 2006) The role of process system engineering (PSE) in integrated circuit (IC) manufacturing. Control Eng Pract 15(7):793-802 Special Issue on Award Winning Applications, 2005 IFAC World Congress
69. Louchet J (June 2001) Using an individual evolution strategy for stereovision. Genet Programming Evol Mach 2(2):101-109
70. Lutton E, Levy-Vehel J, Cretin G, Glevarec P, Roll C (1995) Mixed IFS: Resolution of the inverse problem using genetic programming. Research Report No 2631, INRIA
71. Machado P, Romero J (eds). (2008) The Art of Artificial Evolution. Springer
72. McPhee NF, Miller JD (1995) Accurate replication in genetic programming. In: Eshelman L (ed), Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), Pittsburgh, PA 15-19 July Morgan Kaufmann pp. 303-309
73. Miller J (2001) What bloat? Cartesian genetic programming on Boolean problems. In: Goodman ED (ed), 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers, pp. 295-302, San Francisco, CA 9-11 July
74. Mitavskiy B, Rowe J (2006) Some results about the Markov chains associated to GPs and to general EAs. Theor Comput Sci 361(1):72-110 28 Aug
75. Mitchell M, Forrest S, Holland J (1992) The royal road for genetic algorithms: Fitness landscapes and GA performance. In: Varela F, Bourgine P (eds), Toward a Practice of Autonomous Systems, Proceedings of the First European Conference on Artificial Life, The MIT Press, pp. 245-254
76. Nikolaev NI, Slavov V (1998) Concepts of inductive genetic programming. In: Banzhaf, W., et al. (ed), Genetic Programming, Proceedings of EuroGP'1998, vol 1391 of LNCS, Springer-Verlag, pp. 49-59
77. Nordin P, Banzhaf W (1996) Programmatic compression of images and sound. In: Koza JR, et al. (eds), Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA 28-31 July. MIT Press pp. 345-350
78. Poli R (1996) Genetic programming for image analysis. In: Koza JR et al. (eds), Genetic Programming 1996:Proceedings of the First Annual Conference, Stanford University, CA 28-31 July MIT Press pp. 363-368
79. Poli R (2001) Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover. Genet Programming Evol Mach 2(2):123-163
80. Poli R, Langdon WB (1997) Genetic programming with one-point crossover and point mutation. Tech. Rep. CSRP-97-13, University of Birmingham, B15 2TT, U. K., 15
81. Poli R, Langdon WB (2006) Efficient Markov chain model of machine code program execution and halting. In: Riolo RL, et al. (eds), Genetic Programming Theory and Practice IV, vol 5 of Genetic and Evolutionary Computation, chapter 13. Springer, Ann Arbor, 11-13 May
82. Poli R, Langdon WB, Dignum S (2007) On the limiting distribution of program sizes in tree-based genetic programming. In: Ebner, M et al. (eds), Proceedings of the 10th European Conference on Genetic Programming, vol 4445 of Lecture Notes in Computer Science, Valencia, Spain, 11-13 Apr. Springer pp. 193-204
83. Poli R, McPhee NF (Mar. 2003a) General schema theory for genetic programming with subtree-swapping crossover: Part I. Evol Comput 11(1):53-66
84. Poli R, McPhee NF (June 2003b) General schema theory for genetic programming with subtree-swapping crossover: Part II. Evol Comput 11(2):169-206
85. Poli R, McPhee NF (2008) Parsimony pressure made e. In: GECCO '08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1267-1274, New York, NY, ACM
86. Poli R, McPhee NF, Rowe JE (Mar. 2004) Exact schema theory and Markov chain models for genetic programming and variable-length genetic algorithms with homologous crossover. Genet Programming Evol Mach 5(1):31-70
87. Poli R, McPhee NF, Graff M (2009) Free lunches for symbolic regression. In: Foundations of Genetic Algorithms (FOGA). ACM, forthcoming
88. Poli R, Langdon WB, McPhee NF (2008) A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-fieldguide. org.uk, (With contributions by J. R. Koza)
89. Rissanen J (1978) Modeling by shortest data description. Automatica 14:465-471
90. RochatD, Tomassini M, Vanneschi L (2005) Dynamic size populations in distributed genetic programming. In: Keijzer M, et al. (eds), Proceedings of the 8th European Conference on Genetic Programming, vol 3447 of Lecture Notes in Computer Science, Lausanne, Switzerland, 30 Mar.-1 Apr. Springer. pp. 50-61
91. Rosca JP (1995) Towards automatic discovery of building blocks in genetic programming. In: Working Notes for the AAAI Symposium on Genetic Programming, AAAI, pp. 78-85
92. Rudolph G (1994) Convergence analysis of canonical genetic algorithm. IEEE Trans Neural Netw 5(1):96-101
93. Rudolph G (1996) Convergence of evolutionary algorithms in general search spaces. In: International Conference on Evolutionary Computation, pp. 50-54
94. Schumacher C, Vose MD, Whitley LD (2001) The no free lunch and problem description length. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Morgan Kaufmann, pp. 565-570
95. Seront G (1995) External concepts reuse in genetic programming. In: Siegel EV, Koza JR (eds), Working Notes for the AAAI Symposium on Genetic Programming, MIT, Cambridge, MA 10-12 Nov. AAAI pp. 94-98
96. Shah SC, Kusiak A (July 2004) Data mining and genetic algorithm based gene/SNP selection. Artif Intell Med 31(3):183-196
97. Silva S (2008) Controlling bloat: individual and population based approaches in genetic programming. Ph. D. thesis, Universidade de Coimbra, Faculdade de Ciences e Tecnologia, Departamento de Engenharia Informatica, Portugal
98. Smola AJ, Scholkopf B (1999) A tutorial on support vector regression. Technical Report Technical Report Series-NC2-TR-1998-030, NeuroCOLT2
99. Soule T, Foster JA (1998a) Effects of code growth and parsimony pressure on populations in genetic programming. Evol Comput 6(4):293-309, Winter
100. Soule T, Foster JA (1998b) Removal bias: A new cause of code growth in tree based evolutionary programming. In 1998 IEEE International Conference on Evolutionary Computation, Anchorage, Alaska 5-9 May IEEE Press. pp. 781-186
101. Spector L (1995) Evolving control structures with automatically defined macros. In: Siegel EV, Koza JR (eds), Working Notes for the AAAI Symposium on Genetic Programming, MIT, Cambridge, MA 10-12 Nov. AAAI pp. 99-105
102. Stadler PF (2002) Fitness landscapes. In: Lässig M, Valleriani A (eds), Biological Evolution and Statistical Physics, vol 585 of LectureNotes Physics, pp. 187-207, Heidelberg, Springer-Verlag
103. Stephens CR, Waelbroeck H (1999) Schemata evolution and building blocks. Evol Comput 7(2):109-124
104. Tomassini M, Vanneschi L, Cuendet J, Fernandez F (2004) A new technique for dynamic size populations in genetic programming. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, Portland, Oregon, 20-23 June. IEEE Press pp. 486-493
105. Tomassini M, Vanneschi L, Collard P, Clergue M (2005) A study of fitness distance correlation as a difficultymeasure in genetic programming. Evol Comput 13(2):213-239, Summer
106. Trujillo L, Olague G (2006) Using evolution to learn how to perform interest point detection. In: X Y T et al. (ed), ICPR 2006 18th International Conference on Pattern Recognition, vol 1, IEEE, pp. 211-214. 20-24 Aug
107. Tsang E, Jin N (2006) Incentive method to handle constraints in evolutionary. In: Collet P, et al. (eds), Proceedings of the 9th European Conference on Genetic Programming, vol 3905 of Lecture Notes in Computer Science, Budapest, Hungary, 10-12 Apr. Springer. pp. 133-144
108. Vanneschi L (2004) Theory and practice for efficient genetic programming Ph. D. thesis, Faculty of Sciences, University of Lausanne, Switzerland
109. Vanneschi L (2007) Investigating problem hardness of real life applications. In: R. R. et al., (ed), Genetic Programming Theory and Practice V, Springer, Computer Science Collection, pp. 107-124, Chapter 7
110. Vanneschi L, Clergue M, Collard P, Tomassini M, Vérel S (2004) Fitness clouds and problem hardness in genetic programming. In: Deb K, et al. (eds), Genetic and Evolutionary Computation-GECCO-2004, Part II, vol 3103 of Lecture Notes in Computer Science Seattle, WA 26-30 June, Springer-Verlag pp. 690-701
111. Vanneschi L, Gustafson S, Mauri G (2006) Using subtree crossover distance to investigate genetic programming dynamics. In: Collet, P., et al. (ed), Genetic Programming, 9th European Conference, EuroGP2006, Lecture Notes in Computer Science, LNCS 3905, pp. 238-249. Springer, Berlin, Heidelberg, New York
112. Vanneschi L, Mauri G, Valsecchi A, Cagnoni S (2006) Heterogeneous cooperative coevolution: strategies of integration between GP and GA. In: Keijzer M, et al. (eds), GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, vol 1, Seattle, Washington, DC, 8-12 July. ACM Press. pp. 361-368
113. Vanneschi L, Rochat D, Tomassini M (2007) Multioptimization improves genetic programming generalization ability. In: Thierens D, et al. (eds), GECCO '07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, vol 2, London, 7-11 July. ACM Press. pp. 1759-1759
114. Vanneschi L, Tomassini M, Collard P, Clergue M (2003) Fitness distance correlation in structural mutation genetic programming. In: Ryan, C., et al., (ed), Genetic Programming, 6th European Conference, EuroGP2003, Lecture Notes in Computer Science, Springer-Verlag, Heidelberg, pp 455-464
115. Vanneschi L, Tomassini M, Collard P, Vérel S (2006) Negative slope coefficient. A measure to characterize genetic programming. In: Collet P, et al. (eds), Proceedings of the 9th European Conference on Genetic Programming, vol 3905 of Lecture Notes in Computer Science, Budapest, Hungary, 10-12 Apr. Springer. pp. 178-189
116. Wagner N, Michalewicz Z, Khouja M, McGregor RR (Aug. 2007) Time series forecasting for dynamic environments: The DyFor genetic program model. IEEE Trans Evol Comput 11(4):433-452
117. Wang Y, Wineberg M (2006) Estimation of evolvability genetic algorithm and dynamic environments. Genet Programming Evol Mach 7(4):355-382
118. Wedge DC, Kell DB (2008) Rapid prediction of optimum population size in genetic programming using a novel genotype-fitness correlation. In: Keijzer M, et al. (eds), GECCO '08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, Atlanta, GA, ACM pp. 1315-1322
119. Whitley D, Watson JP (2005) Complexity theory and the no free lunch theorem. In: Burke EK, Kendall G (eds), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Chapter 11, pp. 317-339. Springer
120. Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67-82
121. Woodward JR, Neil JR (2003) No free lunch, program induction and combinatorial problems. In: Ryan C, et al. (eds), Genetic Programming, Proceedings of EuroGP'2003, vol 2610 of LNCS, Essex, 14-16 Apr. Springer-Verlag pp. 475-484
122. Xie H, Zhang M, Andreae P (2006) Genetic programming for automatic stress detection in spoken English. In: Rothlauf F, et al. (eds), Applications of Evolutionary Computing, EvoWorkshops 2006: Evo-BIO, EvoCOMNET, EvoHOT, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, vol 3907 of LNCS, pp. 460-471, Budapest, 10-12 Apr. Springer Verlag
123. Yang S, Ong Y-S, Jin Y (Dec. 2006) Editorial to special issue on evolutionary computation in dynamic and uncertain environments. Genet Programming Evol Mach 7(4):293-294, Editorial
124. Yu T, Chen S-H (2004) Using genetic programming with lambda abstraction to find technical trading rul. In: Computing in Economics and Finance, University of Amsterdam, 8-10 July
125. Yu J, Yu J, Almal AA, Dhanasekaran SM, Ghosh D, Worzel WP, Chinnaiyan AM (Apr. 2007) Feature selection and molecular classification of cancer using genetic programming. Neoplasia 9(4):292-303
126. Zhang B-T, Mühlenbein H (1995) Balancing accuracy and parsimony in genetic programming. Evol Comput 3(1):17-38
127. Zhang M, Smart W (Aug. 2006) Using gaussian distribution to construct fitness functions in genetic programming for multiclass object classification. Pattern Recog Lett 27(11):1266-1274. Evolutionary Computer Vision and Image Understanding