Abstract
The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called “machine intelligence„ (Turing, 1948, 1950). In his talk entitled AI: Where It Has Been and Where It Is Going, machine learning pioneer Arthur Samuel stated the main goal of the fields of machine learning and artificial intelligence: [T]he aim [is]... to get machines to exhibit behavior, which if done by humans, would be assumed to involve the use of intelligence. (Samuel, 1983) Genetic programming is a systematic method for getting computers to automatically solve a problem starting from a high-level statement of what needs to be done. Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a problem. Specifically, genetic programming iteratively transforms a population of computer programs into a new generation of programs by applying analogs of naturally occurring genetic operations. This process is illustrated in Figure 5.1.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Andre, D. and Teller, A., 1999, Evolving team Darwin United, in: RoboCup-98: Robot Soccer World Cup II, M. Asada, and H. Kitano, ed., Lecture Notes in Computer Science, Vol. 1604, Springer, Berlin, pp. 346–352.
Angeline, P. J. and Kinnear Jr, K. E., eds, 1996, Advances in Genetic Programming 2, MIT Press, Cambridge, MA.
Babovic, V., 1996, Emergence, Evolution, Intelligence: Hydroinformatics, Balkema, Rotterdam.
Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M. and Smith, R. E., eds, 1999, GECCO-99: Proc. Genetic and Evolutionary Computation Conf. (Orlando, FL), Morgan Kaufmann, San Mateo, CA.
Banzhaf, W., Nordin, P., Keller, R. E. and Francone, F. D., 1998a, Genetic Programming: An Introduction, Morgan Kaufmann, San Mateo, CA.
Banzhaf, W., Poli, R., Schoenauer, M. and Fogarty, T. C., 1998b, Genetic Programming: Proc. 1st Eur. Workshop (Paris), Lecture Notes in Computer Science. Vol. 1391, Springer, Berlin.
Barnum, H., Bernstein, H. J., and Spector, L., 2000, Quantum circuits for OR and AND of ORs, J. Phys. A: Math. Gen. 33:8047–8057.
Blickle, T., 1997, Theory of Evolutionary Algorithms and Application to System Synthesis, TIK-Schriftenreihe Nr. 17. Zurich, Switzerland: vdf Hochschul, AG an der ETH, Zurich.
Foster, J. A., Lutton, E., Miller, J., Ryan, C. and Tettamanzi, A. G. B., eds, 2002, Genetic Programming: Proc. 5th Eur. Conf., EuroGP 2002 (Kinsale, Ireland).
Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.
Holland, J. H., 1975, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press, Ann Arbor, MI (reprinted 1992, MIT Press, Cambridge, MA).
Iba, H., 1996, Genetic Programming, Tokyo Denki University Press, Tokyo, in Japanese.
Jacob, C., 1997, Principia Evolvica: Simulierte Evolution mit Mathematica, dpunkt.verlag, Heidelberg.
Jacob, C., 2001, Illustrating Evolutionary Computation with Mathematica, Morgan Kaufmann, San Mateo, CA.
Kinnear, K. E. Jr, ed., 1994, Advances in Genetic Programming, MIT Press, Cambridge, MA.
Koza, J. R., 1989, Hierarchical genetic algorithms operating on populations of computer programs, in: Proc. 11th Int. Joint Conf. on Artificial Intelligence, Vol. 1, Morgan Kaufmann, San Mateo, CA, pp. 768–774.
Koza, J. R., 1990, Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems, Stanford University Computer Science Department Technical Report STAN-CS-90-1314.
Koza, J. R., 1992, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA.
Koza, J. R., 1994a, Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, Cambridge, MA.
Koza, J. R., 1994b, Genetic Programming II Videotape: The Next Generation, MIT Press, Cambridge, MA.
Koza, J. R., 1994c, Architecture-altering operations for evolving the architecture of a multi-part program in genetic programming, Stanford University Computer Science Department Technical Report STAN-CS-TR-94-1528.
Koza, J. R., 1995, Gene duplication to enable genetic programming to concurrently evolve both the architecture and work-performing steps of a computer program, in: Proc. 14th Int. Joint Conf. on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, pp. 734–740.
Koza, J. R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D. B., Garzon, M. H., Goldberg, D. E., Iba, H. and Riolo, R., eds, 1998, Genetic Programming 1998: Proc. 3rd Annual Conf. (Madison, WI), Morgan Kaufmann, San Mateo, CA.
Koza, J. R., Bennett III, F. H, Andre, D. and Keane, M. A., 1999a, Genetic Programming III: Darwinian Invention and Problem Solving, Morgan Kaufmann, San Mateo, CA.
Koza, J. R., Bennett III, F. H., Andre, D., Keane, M. A. and Brave, S., 1999b, Genetic Programming III Videotape: Human-Competitive Machine Intelligence, Morgan Kaufmann, San Mateo, CA.
Koza, J. R., Deb, K., Dorigo, M., Fogel, D. B., Garzon, M., Iba, H. and Riolo, R. L., eds, Genetic Programming 1997: Proc. 2nd Annual Conf. (Stanford University), Morgan Kaufmann, San Mateo, CA.
Koza, J. R., Goldberg, D. E., Fogel, D. B. and Riolo, R. L., eds, 1996, Genetic Programming 1996: Proc. 1st Annual Conf. (Stanford University), MIT Press, Cambridge, MA.
Koza, J. R., Keane, M. A., Streeter, M. J., Mydlowec, W., Yu, J. and Lanza, G., 2003, Genetic Programming IV: Routine Human-Competitive Machine Intelligence, Kluwer, Dordrecht.
Koza, J. R. and Rice, J. P., 1992, Genetic Programming: The Movie, MIT Press, Cambridge, MA.
Langdon, W. B., 1998, Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! Kluwer, Amsterdam.
Langdon, W. B., Cantu-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M. A., Schultz, A. C., Miller, J. F., Burke, E. and Jonoska, N., eds, 2002, Proc. 2002 Genetic and Evolutionary Computation Conf., Morgan Kaufmann, San Mateo, CA.
Langdon, W. B. and Poli, R., 2002, Foundations of Genetic Programming, Springer, Berlin.
Luke, S., 1998, Genetic programming produced competitive soccer softbot teams for RoboCup97, in: Genetic Programming 1998: Proc. 3rd Annual Conf. (Madison, WI), J. R. Koza, W. Banzhaf, K. Chellapilla, D. Kumar, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba and R. Riolo, eds, Morgan Kaufmann, San Mateo, CA, pp. 214–222.
Miller, J., Tomassini, M., Lanzi, P. L., Ryan, C., Tettamanzi, A. G. B. and Langdon, W. B., eds, 2001, Genetic Programming: Proc. 4th Eur. Conf., EuroGP 2001 (Lake Como, Italy), Springer, Berlin.
Nordin, P., 1997, Evolutionary Program Induction of Binary Machine Code and Its Application, Krehl, Munster.
Poli, R. and Langdon, W. B., 1997, A new schema theory for genetic programming with one-point crossover and point mutation, in: Genetic Programming 1997: Proc. 2nd Annual Conf. (Stanford University), J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba and R. L. Riolo, R. L., eds, Morgan Kaufmann, San Mateo, CA, pp. 278–285.
Poli, R, and Langdon, W. B., 1998, Schema theory for genetic programming with one-point crossover and point mutation, Evol. Comput. 6:231–252.
Poli, R, and McPhee, N. F., 2001, Exact schema theorems for GP with one-point and standard crossover operating on linear structures and their application to the study of the evolution of size, in: Genetic Programming, Proc. EuroGP 2001, Lake Como, Italy, J. F. Miller, M. Tomassini, P. L. Lanzi, C. Ryan, A. G. B. Tettamanzi and W. B. Langdon, eds, Lecture Notes in Computer Science, Vol. 2038, Springer, Berlin, pp. 126–142.
Poli, R. and N. F., McPhee, 2003a, General schema theory for genetic programming with subtree-swapping crossover: Part I, Evol. Comput. 11:53–66.
Poli, R. and N. F., McPhee, 2003b, General schema theory for genetic programming with subtree-swapping crossover: Part II, Evol. Comput. 11:169–206.
Poli, R., Nordin, P., Langdon, W. B. and Fogarty, T. C., 1999, Genetic Programming: Proc. 2nd Eur. Workshop, EuroGP’99, Lecture Notes in Computer Science. Vol. 1598, Springer, Berlin.
Poli, R., Banzhaf, W., Langdon, W. B., Miller, J., Nordin, P. and Fogarty, T. C, 2000, Genetic Programming: Proc. Eur. Conf., EuroGP 2000 (Edinburgh), Lecture Notes in Computer Science. Vol. 1802, Springer, Berlin.
Ryan, C., 1999, Automatic Re-engineering of Software Using Genetic Programming, Kluwer, Amsterdam.
Samuel, A. L., 1983, AI: Where it has been and where it is going, in: Proc. 8th Int. Joint Conf. on Artificial Intelligence, Los Altos, CA, Morgan Kaufmann, San Mateo, CA, pp. 1152–1157.
Spector, L., Barnum, H. and Bernstein, H. J., 1998, Genetic programming for quantum computers, in: Genetic Programming 1998: Proc. 3rd Annual Conf. (Madison, WI), J. R. Koza, W. Banzhaf, K. Chellapilla, D. Kumar, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba and R. Riolo, eds, Morgan Kaufmann, San Mateo, CA, pp. 365–373.
Spector, L., Barnum, H. and Bernstein, H. J., 1999a, Quantum computing applications of genetic programming, in: Advances in Genetic Programming 3, L. Spector, W. B. Langdon, U.-M. O’Reilly and P. Angeline, eds, MIT Press, Cambridge, MA, pp. 135–160.
Spector, L., Barnum, H., Bernstein, H. J. and Swamy, N., 1999b, Finding a better-than-classical quantum AND/OR algorithm using genetic programming, in: IEEE Proc. 1999 Congress on Evolutionary Computation, IEEE, Piscataway, NJ, pp. 2239–2246.
Spector, L. and Bernstein, H. J., 2002, Communication capacities of some quantum gates, discovered in part through genetic programming, in: Proc. 6th Int. Conf on Quantum Communication, Measurement, and Computing (Rinton, Paramus, NJ).
Spector, L., Goodman, E., Wu, A., Langdon, W. B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M. and Burke, E., eds, 2001, Proc. Genetic and Evolutionary Computation Conf, GECCO-2001, Morgan Kaufmann, San Mateo, CA.
Stephens, C. R. and Waelbroeck, H., 1997, Effective degrees of freedom in genetic algorithms and the block hypothesis, in: Genetic Algorithms: Proc. 7th Int. Conf., Thomas Back, ed., Morgan Kaufmann, San Mateo, CA, pp. 34–40.
Stephens, C. R. and Waelbroeck, H., 1999, Schemata evolution and building blocks, Evol. Comput. 7:109–124.
Turing, A. M., 1948, Intelligent machinery. Reprinted in: 1992, Mechanical Intelligence: Collected Works of A. M. Turing, D. C. Ince, ed., North-Holland, Amsterdam, pp. 107–127. Also reprinted in: 1969, Machine Intelligence 5, B. Meltzer, and D. Michie, ed., Edinburgh University Press, Edinburgh.
Turing, A. M., 1950, Computing machinery and intelligence, Mind 59:433–460. Reprinted in: 1992, Mechanical Intelligence: Collected Works of A. M. Turing, D. C. Ince, ed., North-Holland, Amsterdam, pp. 133–160.
Whitley, L. D., 1994, A genetic algorithm tutorial, Statist. Comput. 4:65–85.
Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I. and Beyer, H.-G., eds, 2000, GECCO-2000: Proc. Genetic and Evolutionary Computation Conf. (Las Vegas, NV), Morgan Kaufmann, San Mateo, CA.
Wong, M. L. and Leung, K. S., 2000, Data Mining Using Grammar Based Genetic Programming and Applications, Kluwer, Amsterdam.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Koza, J.R., Poli, R. (2005). Genetic Programming. In: Burke, E.K., Kendall, G. (eds) Search Methodologies. Springer, Boston, MA. https://doi.org/10.1007/0-387-28356-0_5
Download citation
DOI: https://doi.org/10.1007/0-387-28356-0_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23460-1
Online ISBN: 978-0-387-28356-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)