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Title: Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms.
Authors: Petrovski, Andrei
McCall, John
Editors: Zitzler, Eckhart
Deb, Kalanmoy
Thiele, Lothar
Coello, Carlos A.
Corne, David
Issue Date: 2001
Publisher: Springer
Citation: PETROVSKI, A. and MCCALL, J., 2001. Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. In: ZITZLEF, E., DEB, K., THIELE, L., COELLO, C. and CORNE, D. Evolutionary Multi-criterion Optimization : First International Conference, EMO 2001, Zurich, Switzerland, March 2001 : Proceedings. Berlin: Springer. pp. 531-545.
Series/Report no.: LNCS
1993
Abstract: The main objectives of cancer treatment in general, and of cancer chemotherapy in particular, are to eradicate the tumour and to prolong the patient survival time. Traditionally, treatments are optimised with only one objective in mind. As a result of this, a particular patient may be treated in the wrong way if the decision about the most appropriate treatment objective was inadequate. To partially alleviate this problem, we show in this paper how the multi-objective approach to chemotherapy optimisation can be used. This approach provides the oncologist with versatile treatment strategies that can be applied in ambiguous cases. However, the conflicting nature of treatment objectives and the non-linearity of some of the constraints imposed on treatment schedules make it difficult to utilise traditional methods of multiobjective optimisation. Evolutionary Algorithms (EA), on the other hand, are often seen as the most suitable method for tackling the problems exhibiting such characteristics. Our present study proves this to be true and shows that EA are capable of finding solutions undetectable by other optimisation techniques.
ISBN: 3540417451
Appears in Collections:Conference publications (Computing)

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