|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Author(s):||Godley, Paul Michael|
|Title:||Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches|
|Citation:||Godley PM, Cowie J, Cairns D, McCall J & Howie C (2008) Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches In: IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE World Congress on Computational Intelligence. IEEE Congress on Evolutionary Computation 2008, CEC 2008, Piscataway, NJ, 01.06.2008-06.06.2008. Piscataway, NJ: IEEE (Institute of Electrical and Electronics Engineers), pp. 2532-2537. http://ieeexplore.ieee.org/servlet/opac?punumber=4625778; https://doi.org/10.1109/CEC.2008.4631138.|
|Series/Report no.:||IEEE World Congress on Computational Intelligence|
|Conference Name:||IEEE Congress on Evolutionary Computation 2008, CEC 2008|
|Conference Dates:||2008-06-01 - 2008-06-06|
|Conference Location:||Hong Kong|
|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.|
|Status:||AM - Accepted Manuscript|
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|CEC08FinalAuthor.pdf||Fulltext - Accepted Version||80.53 kB||Adobe PDF||Under Embargo until 2078-06-01 Request a copy|
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