Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

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

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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.
Original languageEnglish
Title of host publication2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
PublisherIEEE
Pages2532-2537
Number of pages6
ISBN (Print)9781424418220
DOIs
Publication statusPublished - 2008

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Keywords

  • Genetic Algorithm
  • Chemotherapy
  • Crossover
  • Optimal Control

Cite this

Godley, P., Cowie, J., Cairns, D., McCall, J., & Howie, C. (2008). Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) (pp. 2532-2537). IEEE. https://doi.org/10.1109/CEC.2008.4631138