From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Optimizing Chemotherapy Scheduling Using Local Search Heuristics
Operations Research
An evolutionary approach to cancer chemotherapy scheduling
Genetic Programming and Evolvable Machines
Automating the drug scheduling of cancer chemotherapy via evolutionary computation
Artificial Intelligence in Medicine
Structure learning and optimisation in a Markov-network based estimation of distribution algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A dynamical model of cancer chemotherapy with disturbance
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
On the taxonomy of optimization problems under estimation of distribution algorithms
Evolutionary Computation
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Chemotherapy treatment for cancer is a complex optimisation problem with a large number of interacting variables and constraints. A number of different heuristics have been applied to it with varying success. In this paper we expand on this by applying two estimation of distribution algorithms to the problem. One is UMDA and the other is hBOA, the first EDA using a multivariate probabilistic model to be applied to the chemotherapy problem. While instinct would lead us to predict that the more sophisticated algorithm would yield better performance on a complex problem like this, we show that it is outperformed by the algorithms using the simpler univariate model. We hypothesise that this is caused by the more sophisticated algorithm being impeded by the large number of interactions in the problem which though present, do not complicate the search for optima.