2 research outputs found

    The Effects of Randomly Sampled Training Data on Program Evolution

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    The effects of randomly sampled training data on genetic programming performance is empirically investigated. Often the most natural, if not only, means of characterizing the target behaviour for a problem is to randomly sample training cases inherent to that problem. A natural question to raise about this strategy is, how deleterious is the randomly sampling of training data to evolution performance? Will sampling reduce the evolutionary search to hill climbing? Can resampling during the run be advantageous? We address these questions by undertaking a suite of different GP experiments. Parameters include various sampling strategies (single, re-sampling, ideal samples), generational and steady-state evolution, and non-evolutionary strategies such as hill climbing and random search. The experiments confirm that random sampling effectively characterizes stochastic domains during genetic programming, provided that a sufficiently representative sample is used. An u..

    The Effects of Randomly Sampled Training Data on Program Evolution

    No full text
    The effects of randomly sampled training data during genetic programming is empirically investigated. Sometimes the most natural, if not only, means of characterizing the target behaviour for some problems is to randomly sample training cases inherent to the problems in question. A natural question to raise about this strategy is, how deleterious is the randomly sampling of training data to evolution performance? Would such sampling reduce the evolutionary search to hill climbing? We address these questions by undertaking a suite of different GP experiments. Various sampling strategies are used, such as different training set sizes, single and multiple samples per run, and manually derived "ideal distribution" training sets. Both generational and steady--state evolution are tested, in order to see if random sampling particularly affects one or the other. Non-- evolutionary search strategies, such as hill climbing and random search, are also used for comparison. Th..
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