Thomas Loveard

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This paper investigates the unexpected convergence behaviour of genetic Programming (GP) for classification problems. Firstly the paper investigates the relationship between computational effort and attainable classification accuracy. Secondly we attempt to understand why GP classifiers sometimes fail to reach satisfactory levels of accuracy for certain(More)
The genetic programming (GP) search method can often vary greatly in the quality of solution derived from one run to the next. As a result, it is often the case that a number of runs must be performed to ensure that an effective solution is found. This paper introduces several methods which attempt to better utilise the computational resources spent on(More)
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