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Genetic programming

 
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A type of programming that imitates genetic algorithms, which uses mutation and replication to produce algorithms that represent the "survival of the fittest." While genetic algorithms yield numbers, genetic programs yield ever-improving computer programs. Written in languages such as LISP and Scheme, genetic programming requires the determination of a fitness function, which is a desired output (result). The degree of error in the fitness function determines the quality of the program. For more information, visit www.geneticprogramming.com.

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Wikipedia: Genetic programming
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In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. Therefore it is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.

Contents

Chromosome representation

A function represented as a tree structure.

GP evolves computer programs, traditionally represented in memory as tree structures[1]. Trees can be easily evaluated in a recursive manner. Every tree node has an operator function and every terminal node has an operand, making mathematical expressions easy to evolve and evaluate. Thus traditionally GP favors the use of programming languages that naturally embody tree structures (for example, Lisp; other functional programming languages are also suitable).

Non-tree representations have been suggested and successfully implemented, such as linear genetic programming which suits the more traditional imperative languages [see, for example, Banzhaf et al. (1998)]. The commercial GP software Discipulus, uses AIM, automatic induction of binary machine code[2] to achieve better performance.[3] MicroGP[4] uses a representation similar to linear GP to generate programs that fully exploit the syntax of a given assembly language.

See also

References and notes

  1. ^ Cramer, 1985
  2. ^ (Peter Nordin, 1997, Banzhaf et al., 1998, Section 11.6.2-11.6.3)
  3. ^ aigp3.dvi
  4. ^ Research: MicroGP

Bibliography

  • Banzhaf, W., Nordin, P., Keller, R.E., and Francone, F.D. (1998), Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications, Morgan Kaufmann
  • Barricelli, Nils Aall (1954), Esempi numerici di processi di evoluzione, Methodos, pp. 45-68.
  • Brameier, M. and Banzhaf, W. (2007), Linear Genetic Programming, Springer, New York
  • Crosby, Jack L. (1973), Computer Simulation in Genetics, John Wiley & Sons, London.
  • Cramer, Nichael Lynn (1985), "A representation for the Adaptive Generation of Simple Sequential Programs" in Proceedings of an International Conference on Genetic Algorithms and the Applications, Grefenstette, John J. (ed.), Carnegie Mellon University
  • Fogel, David B. (2000) Evolutionary Computation: Towards a New Philosophy of Machine Intelligence IEEE Press, New York.
  • Fogel, David B. (editor) (1998) Evolutionary Computation: The Fossil Record, IEEE Press, New York.
  • Forsyth, Richard (1981), BEAGLE A Darwinian Approach to Pattern Recognition Kybernetes, Vol. 10, pp. 159-166.
  • Fraser, Alex S. (1957), Simulation of Genetic Systems by Automatic Digital Computers. I. Introduction. Australian Journal of Biological Sciences vol. 10 484-491.
  • Fraser, Alex and Donald Burnell (1970), Computer Models in Genetics, McGraw-Hill, New York.
  • Holland, John H (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor
  • Korns, Michael (2007), Large-Scale, Time-Constrained, Symbolic Regression-Classification, in Genetic Programming Theory and Practice V. Springer, New York.
  • Korns, Michael (2009), Symbolic Regression of Conditional Target Expressions, in Genetic Programming Theory and Practice VII. Springer, New York.
  • 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. A thorough report, possibly used as a draft to his 1992 book.
  • Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press
  • Koza, J.R. (1994), Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press
  • Koza, J.R., Bennett, F.H., Andre, D., and Keane, M.A. (1999), Genetic Programming III: Darwinian Invention and Problem Solving, Morgan Kaufmann
  • Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G. (2003), Genetic Programming IV: Routine Human-Competitive Machine Intelligence, Kluwer Academic Publishers
  • Langdon, W. B., Poli, R. (2002), Foundations of Genetic Programming, Springer-Verlag
  • Nordin, J.P., (1997) Evolutionary Program Induction of Binary Machine Code and its Application. Krehl Verlag, Muenster, Germany.
  • Poli, R., Langdon, W. B., McPhee, N. F. (2008). A Field Guide to Genetic Programming. Lulu.com, freely available from the internet. ISBN 978-1-4092-0073-4. 
  • Rechenberg, I. (1971): Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis). Reprinted by Fromman-Holzboog (1973).
  • Schmidhuber, J. (1987). Evolutionary principles in self-referential learning. (On learning how to learn: The meta-meta-... hook.) Diploma thesis, Institut f. Informatik, Tech. Univ. Munich.
  • Smith, S.F. (1980), A Learning System Based on Genetic Adaptive Algorithms, PhD dissertation (University of Pittsburgh)
  • Smith, Jeff S. (2002), Evolving a Better Solution, Developers Network Journal, March 2002 issue
  • Shu-Heng Chen et al. (2008), Genetic Programming: An Emerging Engineering Tool,International Journal of Knowledge-based Intelligent Engineering System, 12(1): 1-2, 2008.

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