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Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Authors

  • Paul Godley
  • Julie Cowie
  • David Cairns
  • John McCall
  • Catherine Howie

Research units

Info

Original languageEnglish
Title IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence)
Place of publicationNew York
PublisherIEEE Computer Society
Publication date2008
Pages2532-2537
Number of pages6
ISBN (Print)9781424418220
DOIs
StatePublished

Conference

Conference2008 IEEE Congress on Evolutionary Computation
CountryChina
CityHong Kong
Period1/06/086/06/08
OtherHeld as part of WCCI 2008 - the joint event of 2008 International Joint Conference on Neural Networks (IJCNN 2008), 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008), and 2008 IEEE Congress on Evolutionary Computation (CEC 2008).
Internet addresshttp://www2.mae.cuhk.edu.hk/~wcci2008/

Abstract

This paper describes two directed intervention crossover approaches that are applied to the problem of deriving optimal cancer chemotherapy treatment schedules. Unlike traditional uniform crossover (UC), both the calculated expanding bin (CalEB) method and targeted intervention with stochastic selection (TInSSel) approaches actively choose an intervention level and spread based on the fitness of the parents selected for crossover. Our results indicate that these approaches lead to significant improvements over UC when applied to cancer chemotherapy scheduling.

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