2 resultados para fitness

em Greenwich Academic Literature Archive - UK


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The paper describes the design of an efficient and robust genetic algorithm for the nuclear fuel loading problem (i.e., refuellings: the in-core fuel management problem) - a complex combinatorial, multimodal optimisation., Evolutionary computation as performed by FUELGEN replaces heuristic search of the kind performed by the FUELCON expert system (CAI 12/4), to solve the same problem. In contrast to the traditional genetic algorithm which makes strong requirements on the representation used and its parameter setting in order to be efficient, the results of recent research results on new, robust genetic algorithms show that representations unsuitable for the traditional genetic algorithm can still be used to good effect with little parameter adjustment. The representation presented here is a simple symbolic one with no linkage attributes, making the genetic algorithm particularly easy to apply to fuel loading problems with differing core structures and assembly inventories. A nonlinear fitness function has been constructed to direct the search efficiently in the presence of the many local optima that result from the constraint on solutions.

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Nurses need good clinical numeracy skills to aid them in their clinical practice. There is some concern, however, that the calculation skills learned during pre-registration nurse education have little practical application to nurses. This article discusses the Fitness for Practice initiatives from the Nursing and Midwifery Council which aim to ensure new registrants are numerate. The article argues that written numeracy assessment tools are not a valid test of the numeracy skills candidates will require for clinical practice and that nurse education needs to focus on researching and examining how best to support, assess and develop the numeracy skills of nursing students within their clinical practice placements to ensure that at the point of registration they are fit for practice.