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Basic Concepts of Cellular Automata Read chapter Read first chapter

2012 | OriginalPaper | Chapter

Genetic Programming — Introduction, Applications, Theory and Open Issues

Authors : Leonardo Vanneschi, Riccardo Poli

Published in: Handbook of Natural Computing

Publisher: Springer Berlin Heidelberg

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Abstract

Genetic programming (GP) is an evolutionary approach that extends genetic algorithms to allow the exploration of the space of computer programs. Like other evolutionary algorithms, GP works by defining a goal in the form of a quality criterion (or fitness) and then using this criterion to evolve a set (or population) of candidate solutions (individuals) by mimicking the basic principles of Darwinian evolution. GP breeds the solutions to problems using an iterative process involving the probabilistic selection of the fittest solutions and their variation by means of a set of genetic operators, usually crossover and mutation. GP has been successfully applied to a number of challenging real-world problem domains. Its operations and behavior are now reasonably well understood thanks to a variety of powerful theoretical results. In this chapter, we introduce the main definitions and features of GP and describe its typical operations. We then survey some of its applications. We also review some important theoretical results in this field, including some very recent ones, and discuss some of the most challenging open issues and directions for future research.

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Literature
Altenberg L (2009) Modularity in evolution: Some low-level questions. In: Rasskin-Gutman D, Callebaut W (eds), Modularity: Understanding the Development and Evolution of Complex Natural Systems. MIT Press, Cambridge, MA
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
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
Azaria Y, Sipper M (2005) GP-gammon: Genetically programming backgammon players. Genet Programming Evol Mach 6(3):283–300, Sept. Published online: 12 August 2005CrossRef
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. 300–309CrossRef
Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic Programming, An Introduction. Morgan Kaufmann, San Francisco, CAMATH
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
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):38–44CrossRef
Brameier M, Banzhaf W (2001) A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans Evol Comput 5(1):17–26CrossRef
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. 1156–1163CrossRef
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
Chen S-H, Liao C-C (2005) Agent-based computational modeling of the stock price-volume relation. Inf Sci 170(1):75–100, 18 FebMathSciNetCrossRef
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
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
Dassau E, Grosman B, Lewin DR (2006) Modeling and temperature control of rapid thermal processing. Comput Chem Eng 30(4):686–697, 15 FebCrossRef
Davis TE, Principe JC (1993) A Markov chain framework for the simple genetic algorithm. Evol Comput 1(3):269–288CrossRef
De Jong KA (1988) Learning with genetic algorithms: An overview. Mach Learn 3:121–138CrossRef
Dempsey I (2007) Grammatical evolution in dynamic environments. Ph.D. thesis, University College Dublin, Ireland
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
Domingos P (1999) The role of Occam's razor in knowledge discovery. Data Mining Knowl Discov 3(4):409–425CrossRef
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
Esparcia-Alcazar AI, Sharman KC (Sept. 1996) Genetic programming techniques that evolve recurrent neural networks architectures for signal processing. In: IEEE Workshop on Neural Networks for Signal Processing, Seiko, Kyoto, Japan
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
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
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
Forrest S, Mitchell M (1993) What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation. Mach Learn 13:285–319CrossRef
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
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
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
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
Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston, MAMATH
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
Gustafson S, Vanneschi L (2008) Operator-based tree distance in genetic programming. IEEE Trans Evol Comput 12:4CrossRef
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 ClustersCrossRef
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):e61CrossRef
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
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
Holland JH (1975) Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, MI
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
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
Jacob C (May–June 2000) The art of genetic programming. IEEE Intell Syst 15(3):83–84, May–June
Jacob C (2001) Illustrating Evolutionary Computation with Mathematica. Morgan Kaufmann, San Francisco, CA
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
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
Jones T (1995) Evolutionary algorithms, fitness landscapes and search. Ph.D. thesis, University of New Mexico, Albuquerque
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
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
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 21CrossRef
Kattan A, Poli R (2008) Evolutionary lossless compression with GP-ZIP. 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):259–269CrossRef
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
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
Klappenecker A, May FU (1995) Evolving better wavelet compression schemes. 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):4–9CrossRef
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
Koza JR (1992b) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MAMATH
Koza JR (1994) Genetic Programming II. The MIT Press, Cambridge, MAMATH
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
Koza JR, Bennett FH III, Andre D, Keane MA (1999) Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann, San Francisco, CAMATH
Kushchu I (2002) An evaluation of evolutionary generalization in genetic programming. Artif Intell Rev 18(1):3–14CrossRefMATH
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
Langdon WB, Buxton BF (Sept. 2004) Genetic programming for mining DNA chip data from cancer patients. Genet Programming Evol Mach, 5(3):251–257CrossRef
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
Langdon WB, Poli R (2002) Foundations of Genetic Programming. Springer-Verlag
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. 2621–2628CrossRef
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
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):1819–1831CrossRef
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 CongressCrossRef
Louchet J (June 2001) Using an individual evolution strategy for stereovision. Genet Programming Evol Mach 2(2):101–109CrossRefMATH
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
Machado P, Romero J (eds). (2008) The Art of Artificial Evolution. Springer
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
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
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 AugMathSciNetCrossRefMATH
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
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
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
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
Poli R (2001) Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover. Genet Programming Evol Mach 2(2):123–163MathSciNetCrossRefMATH
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
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
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
Poli R, McPhee NF (Mar. 2003a) General schema theory for genetic programming with subtree-swapping crossover: Part I. Evol Comput 11(1):53–66CrossRef
Poli R, McPhee NF (June 2003b) General schema theory for genetic programming with subtree-swapping crossover: Part II. Evol Comput 11(2):169–206CrossRef
Poli R, McPhee NF (2008) Parsimony pressure made easy. In: GECCO ’08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1267–1274, New York, NY, ACMCrossRef
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):31–70CrossRef
Poli R, McPhee NF, Graff M (2009) Free lunches for symbolic regression. In: Foundations of Genetic Algorithms (FOGA). ACM, forthcoming
Rochat D, 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
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
Rudolph G (1994) Convergence analysis of canonical genetic algorithm. IEEE Trans Neural Netw 5(1):96–101CrossRef
Rudolph G (1996) Convergence of evolutionary algorithms in general search spaces. In: International Conference on Evolutionary Computation, pp. 50–54
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
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
Shah SC, Kusiak A (July 2004) Data mining and genetic algorithm based gene/SNP selection. Artif Intell Med 31(3):183–196CrossRef
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
Smola AJ, Scholkopf B (1999) A tutorial on support vector regression. Technical Report Technical Report Series – NC2-TR-1998-030, NeuroCOLT2
Soule T, Foster JA (1998a) Effects of code growth and parsimony pressure on populations in genetic programming. Evol Comput 6(4):293–309, WinterCrossRef
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
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
Stadler PF (2002) Fitness landscapes. In: Lässig M, Valleriani A (eds), Biological Evolution and Statistical Physics, vol 585 of Lecture Notes Physics, pp. 187–207, Heidelberg, Springer-Verlag
Stephens CR, Waelbroeck H (1999) Schemata evolution and building blocks. Evol Comput 7(2):109–124CrossRef
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
Tomassini M, Vanneschi L, Collard P, Clergue M (2005) A study of fitness distance correlation as a difficulty measure in genetic programming. Evol Comput 13(2):213–239, SummerCrossRef
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
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
Vanneschi L (2004) Theory and practice for efficient genetic programming Ph.D. thesis, Faculty of Sciences, University of Lausanne, Switzerland
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
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
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
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
Vanneschi L, Rochat D, Tomassini M (2007) Multi-optimization 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. 1759–1759CrossRef
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
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
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):433–452CrossRef
Wang Y, Wineberg M (2006) Estimation of evolvability genetic algorithm and dynamic environments. Genet Programming Evol Mach 7(4):355–382CrossRef
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
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
Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRef
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
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: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, vol 3907 of LNCS, pp. 460–471, Budapest, 10–12 Apr. Springer Verlag
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, EditorialCrossRef
Yu T, Chen S-H (2004) Using genetic programming with lambda abstraction to find technical trading rules. In: Computing in Economics and Finance, University of Amsterdam, 8–10 July
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):292–303CrossRef
Zhang B-T, Mühlenbein H (1995) Balancing accuracy and parsimony in genetic programming. Evol Comput 3(1):17–38CrossRef
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 UnderstandingCrossRef
Metadata
Title
Genetic Programming — Introduction, Applications, Theory and Open Issues
Authors
Leonardo Vanneschi
Riccardo Poli
Copyright Year
2012
Publisher
Springer Berlin Heidelberg
Book
Handbook of Natural Computing

Print ISBN: 978-3-540-92909-3

Electronic ISBN: 978-3-540-92910-9

Copyright Year: 2012

DOI
https://doi.org/10.1007/978-3-540-92910-9_24

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