The effects of mutation and directed intervention crossover when applied to scheduling chemotherapy

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The effects of mutation and directed intervention crossover when applied to scheduling chemotherapy

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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

POSTER SESSION: Genetic algorithms posters table of contents

Pages 1105-1106

Year of Publication: 2008

ISBN:978-1-60558-130-9

Authors

Paul M. Godley	University of Stirling, Stirling, Scotland, Uk
David E. Cairns	University of Stirling, Stirling, Scotland, Uk
Julie Cowie	University Of Stirling, Stirling, Scotland, Uk
Kevin M. Swingler	University of Stirling, Stirling, Scotland, Uk
John McCall	The Robert Gordon University, Aberdeen, Scotland, Uk

Sponsors

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

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ACM New York, NY, USA

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ABSTRACT

This paper discusses the effects of mutation and directed intervention crossover approaches when applied to the derivation of cancer chemotherapy treatment schedules. Unlike traditional Uniform Crossover (UC), the directed intervention techniques actively choose the intervention level based on the fitness of the parents selected for crossover. This work describes how directed intervention crossover principles are more robust to mutation and lead to significant improvement over UC when applied to cancer chemotherapy treatment scheduling.

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	Philippe Collard , Cathy Escazut Alessio Gaspar, An Evolutionary Approach For Time Dependant Optimization, Proceedings of the 8th International Conference on Tools with Artificial Intelligence (ICTAI '96), p.2, November 16-19, 1996
	2	P. M. Godley, D. E. Cairns, and J. Cowie. Directed intervention crossover applied to bio-control scheduling. In IEEE CEC 2007: Proceedings of the IEEE Congress On Evolutionary Computation. IEEE press, 2007.
	3	J. McCall, A. Petrovski, and S. Shakya. Evolutionary algorithms for cancer chemotherapy optimization. In G. B. Fogel, D. W. Corne, and Y. Pan, editors, Computational Intelligence in Bioinformatics, chapter 12, pages 265--296. Wiley IEEE Press, 2008.
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