Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

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.

[1]  Julie Cowie,et al.  Maximising the efficiency of bio-control applications utilising genetic algorithms , 2007 .

[2]  Kok Lay Teo,et al.  Optimal Control of Drug Administration in Cancer Chemotherapy , 1993 .

[3]  Andrey V. Savkin,et al.  Optimal control applied to drug administration in cancer chemotherapy: the case of several toxicity constraints , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[4]  Ezequiel A. Di Paolo,et al.  An efficient Genetic Algorithm with uniform crossover for the multi-objective Airport Gate Assignment Problem , 2007, 2007 IEEE Congress on Evolutionary Computation.

[5]  Chilukuri K. Mohan.  Selective crossover: towards fitter offspring , 1998, SAC '98.

[6]  Terence C. Fogarty,et al.  A Comparative Study of Steady State and Generational Genetic Algorithms , 1996, Evolutionary Computing, AISB Workshop.

[7]  Andrei Petrovski,et al.  An application of genetic algorithms to chemotherapy treatment , 1998 .

[8]  Gary B. Fogel,et al.  Evolutionary Algorithms for Cancer Chemotherapy Optimization , 2007 .

[9]  Edmund K. Burke,et al.  An evolutionary approach to cancer chemotherapy scheduling , 2007, Genetic Programming and Evolvable Machines.

[10]  John A. W. McCall,et al.  Optimising Cancer Chemotherapy Using Particle Swarm Optimisation and Genetic Algorithms , 2004, PPSN.

[11]  S. Siegel,et al.  Nonparametric Statistics for the Behavioral Sciences , 2022, The SAGE Encyclopedia of Research Design.

[12]  T. Wheldon.  Mathematical models in cancer research , 1988 .

[13]  Siddhartha Shakya,et al.  Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms , 2006, GECCO '06.

[14]  Andrew M. Tyrrell,et al.  Variable length genetic algorithms with multiple chromosomes on a variant of the Onemax problem , 2006, GECCO.

[15]  Julie Cowie,et al.  Directed intervention crossover applied to bio-control scheduling , 2007, 2007 IEEE Congress on Evolutionary Computation.