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Meta-Genetic Programming: Co-evolving the Operators of Variation

Edmonds, Bruce (2001) Meta-Genetic Programming: Co-evolving the Operators of Variation. [Journal (Paginated)]

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Abstract

The standard Genetic Programming approach is augmented by co-evolving the genetic operators. To do this the operators are coded as trees of indefinite length. In order for this technique to work, the language that the operators are defined in must be such that it preserves the variation in the base population. This technique can varied by adding further populations of operators and changing which populations act as operators for others, including itself, thus to provide a framework for a whole set of augmented GP techniques. The technique is tested on the parity problem. The pros and cons of the technique are discussed.

Item Type:Journal (Paginated)
Keywords:evolution, co-evolution, operators, variation, genetic programming
Subjects:Biology > Theoretical Biology
Computer Science > Artificial Intelligence
Computer Science > Machine Learning
ID Code:1776
Deposited By:Edmonds, Dr Bruce
Deposited On:30 Aug 2001
Last Modified:11 Mar 2011 08:54

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References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

[1] Angeline, P. J. (1995) Adaptive and Self-Adaptive Evolutionary Computations, In M. Palaniswami, et. al. (eds.),

Computational Intelligence: A Dynamic Systems Perspective, Piscataway, NJ: IEEE Press, pp 152-163.

[2] Angeline, P. (1996). Two Self-adaptive Crossover Operators for Genetic Programming. In Angeline, P. and Kinnear, K.

E. (ed.), Advances in Genetic Programming 2, MIT Press, Cambridge, MA, 89-100.

[3] Angeline, P. (1997). Comparing Subtree Crossover with Macromutation. Lecture Notes in Computer Science,

1213:101-111.

[4] Fogarty, T.C. (1989). Varying the probability of mutation in the genetic algorithm. In Schaffer, J. (ed.), Proceedings of

the Third INternational Conference on Genetic Algorithms, Morgan Kaufmann, 104-109.

[5] Fogel, D.B., Fogel, L.J. and Atmar, J.W. (1991). Meta-Evolutionary Programming. In Chen, R. (ed.), Proceedings of the

25th Aslimar Conference on Signals, Systems and Computers, Maple Press, San jose, CA, 540-545.

[6] Holland, J. H. (1985). Properties of the bucket brigade. In Grefenstette, J. J. (ed.), Proceedings of the 1st International

Conference on Genetic Algorithms and their Applications, Lawrence Erlbaum Associates, 1-7.

[7] Kauffman, S. A. (1996). At Home in the Universe: the search for laws of complexity. Penguin, London.

[8] Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press,

Cambridge, MA.

[9] Langdon, W. B. (1997). Fitness Causes Bloat. WSC2 - 2nd On-Line World Conference on Soft Computing in

Engineering Design and Manufacturing, June 1997. Proceedings to be published by Springer-Verlag.

[10] Montana, D.J. (1995). Strongly-typed Genetic Programming. Evolutionary Computation, 3:199-230.

[11] Radcliffe, N.J. and Surry, P.D. (1995). Fundamental Limitations on Search Algorithms - Evolutionary Computing in

Perspective. Lecture Notes in Computer Science, 1000, 275-291.

[12] Sebag, M. and Schoenauer, M. (1994). Controlling crossover through inductive learning. In Davidor, Y. (ed.),

Proceedings of the 3rd Conference on Parallel Problem-solving from Nature, Springer-Verlag, Berlin, 209-218.

[13] Smith, J.E. and Fogarty, T.C. (1997). Operator and Parameter Adaption in Genetic Algorithms. Soft Computing,

1:81-87.

[14] Teller, A. (1996). Evolving Programmers: The Co-evolution of Intelligent Recombination Operators. In Angeline, P. and

Kinnear, K. E. (ed.), Advances in Genetic Programming 2, MIT Press, Cambridge, MA, 45-68.

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