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 multi-objective 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baeck, T., Fogel, D., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press, (1997)
Coello, C.: An Updated Survey of Evolutionary Multiobjective Optimization Techniques: State of the Art and Future Trends. Proceedings of the 1999 Congress on Evolutionary Computation. IEEE Service Center, Washington, D.C., (1999) 3–13
Cassidy, J., McLeod, H.: Is it possible to design a logical development plan for an anticancer drug. Pharmaceutical Medicine, (1995), 9 95–103
Dearnaley, D., et al.: Handbook of adult cancer chemotherapy schedules. The Medicine Group (Education) Ltd., Oxfordshire, (1995)
Fonseca, C., Fleming, P.: An overview of evolutionary algorithms in multi-objective optimization. Evolutionary Computation, (1995) 3(1) 1–16
Martin, R., Teo, K.: Optimal Control of Drug Administration in Cancer Chemotherapy. World Scientific, Singapore New Jersey London Hong Kong (1994)
McCall, J., Petrovski, A.: A Decision Support System for Cancer Chemotherapy Using Genetic Algorithms. Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, Vol. 1. IOS Press (1999) 65–70
McCall, J., Petrovski, A.: OWCH-A Decision Support System for Designing Novel Cancer Chemotherapies. Proceedings of the First International Symposium on Soft Computing in Biomedicine. ICSC Academic Press (1999) 504–510
Murray, J.: Some optimal control problems in cancer chemotherapy with a toxicity limit. Mathematical Biosciences, (1990) 100 49–67
Petrovski, A., McCall, J.: Computational Optimisation of Cancer Chemotherapies Using Genetic Algorithms. In: John, R., Birkhead, R. (eds.): Soft Computing Techniques and Applications. Series on Advances in Soft Computing. Physica-Verlag (1999) 117–122
Petrovski, A.: An Application of Genetic Algorithms to Chemotherapy Treatment. PhD thesis, The Robert Gordon University, Aberdeen, U.K., (1999)
Petrovski, A., Wilson, A., McCall, J.: Statistical identification and optimisation of significant GA factors. Proceedings of the 5th Joint Conference on Information Sciences. Atlantic City, USA, (2000) Vol. 1, 1027–1030
Swan, G.: Role of Optimal Control theory in Cancer Chemotherapy. Mathematical BioScience, (1990) 101 237–284
Veldhuizen, D., Lamont, G.: Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation, (2000) 8(2) 125–147
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multi-objective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, (2000) 8(2) 173–195
Zitzler, E.: Evolutionary Algorithms for Multi-objective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology (ETH), Zurich, (1999)
Wheldon, T.: Mathematical models in cancer research. Adam Hilger, Bristol Philadelphia (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Petrovski, A., McCall, J. (2001). Multi-objective Optimisation of Cancer Chemotherapy Using Evolutionary Algorithms. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_37
Download citation
DOI: https://doi.org/10.1007/3-540-44719-9_37
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41745-3
Online ISBN: 978-3-540-44719-1
eBook Packages: Springer Book Archive