Experiments with indexed FOR-loops in genetic programming

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Experiments with indexed FOR-loops in genetic programming

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 10th annual conference on Genetic and evolutionary computation table of contents

Atlanta, GA, USA

SESSION: Genetic Programming Postes table of contents

Pages 1347-1348

Year of Publication: 2008

ISBN:978-1-60558-130-9

Authors

Gayan Wijesinghe	RMIT University, Melbourne, Australia
Vic Ciesielski	RMIT University, Melbourne, Australia

Sponsors

ACM: Association for Computing Machinery
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

ACM New York, NY, USA

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ABSTRACT

We investigated how indexed FOR-loops, such as the ones found in procedural programming languages, can be implemented in genetic programming. We use them to train programs that learn the repeating unit string of a given regular binary pattern string and can reproduce the learnt pattern to an arbitrary size, specified by a parameter N. We discovered that this particular problem, where the solution needs to scale with multiple size-instances of the problem, is very hard to solve without the help of domain knowledge.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	V. Ciesielski and X. Li. Experiments with explicit for-loops in genetic programming. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pages 494--501, Portland, Oregon, 20-23 June 2004. IEEE Press.
	2	G. Wijesinghe and V. Ciesielski. Using restricted loops in genetic programming for image classification. In D. Srinivasan and L. Wang, editors, 2007 IEEE Congress on Evolutionary Computation, pages 4569--4576, Singapore, 25-28 Sept. 2007. IEEE Computational Intelligence Society, IEEE Press.