Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

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

doi:10.1109/CEC.2008.4631138

Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

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

^*Corresponding author for this work

Yunus Centre

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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 language	English
Title of host publication	2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
Publisher	IEEE
Pages	2532-2537
Number of pages	6
ISBN (Print)	9781424418220
DOIs	https://doi.org/10.1109/CEC.2008.4631138
Publication status	Published - 2008
Event	2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) - Hong Kong, China Duration: 1 Jun 2008 → 6 Jun 2008

Conference

Conference	2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
Country/Territory	China
City	Hong Kong
Period	1/06/08 → 6/06/08

Keywords

Genetic Algorithm
Chemotherapy
Crossover
Optimal Control

ASJC Scopus subject areas

Computational Theory and Mathematics
Theoretical Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.1109/CEC.2008.4631138

Cite this

@inproceedings{7ee31c2a6d514210995ea5f7e4d43678,

title = "Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches",

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.",

keywords = "Genetic Algorithm, Chemotherapy, Crossover, Optimal Control",

author = "Paul Godley and Julie Cowie and David Cairns and John McCall and Catherine Howie",

year = "2008",

doi = "10.1109/CEC.2008.4631138",

language = "English",

isbn = "9781424418220",

pages = "2532--2537",

booktitle = "2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)",

publisher = "IEEE",

address = "United States",

note = "2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) ; Conference date: 01-06-2008 Through 06-06-2008",

}

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)., 4631138, IEEE, pp. 2532-2537, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, China, 1/06/08. https://doi.org/10.1109/CEC.2008.4631138

TY - GEN

T1 - Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

AU - Godley, Paul

AU - Cowie, Julie

AU - Cairns, David

AU - McCall, John

AU - Howie, Catherine

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - Genetic Algorithm

KW - Chemotherapy

KW - Crossover

KW - Optimal Control

U2 - 10.1109/CEC.2008.4631138

DO - 10.1109/CEC.2008.4631138

M3 - Conference contribution

SN - 9781424418220

SP - 2532

EP - 2537

BT - 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

PB - IEEE

T2 - 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

Y2 - 1 June 2008 through 6 June 2008

ER -

Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Documents and Links

Fingerprint

Cite this