P. R. Weller

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Understanding the evolution of a complex genetic algorithm is a non-trivial problem, however, genetic-algorithm visualization is in its infancy. This paper reviews some of the current approaches and presents a new visualization approach based on Sammon mapping. Sammon mapping is a nonlinear mapping of a set of vectors in p-dimensional space to a set in(More)
BACKGROUND Decisions about which patients to admit to intensive care and how long to keep them there are difficult. A flexible computer-based mathematical model which is sensitive to the complexity of intensive care medicine, and which accurately models prognosis, seems highly desirable. METHODS We have created, optimised by genetic algorithms, trained,(More)
The uncertain fate of individual patients in intensive care results from the heterogeneity of case-mix, life threatening insults, and the host response to such insults. These variables preclude an accurate prediction of likely outcome. Logistic regression models incorporating large numbers of those measurements which vary from patient to patient, and relate(More)
Understanding the evolution of a complex genetic algorithm is a non-trivial problem, however, genetic-algorithm visualization is in its infancy. This paper reviews some of the current approaches and presents a new visualization approach based on Sammon mapping. Sammon mapping is a nonlinear mapping of a set of vectors in p-dimensional space to a set in(More)
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