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Improving Genetic Programming for Classification with Lazy Evaluation and Dynamic Weighting

机译：通过惰性评估和动态加权改进分类的遗传规划

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In the standard process of creating classification decision trees with genetic programming, the evaluation process it the most time-consuming part of the whole evolution loop. Here we introduce a lazy evaluation approach of classification decision trees in the evolution process, that does not evaluate the whole population but evaluates only the individuals that are chosen to participate in the tournament selection method. Further on, we used dynamic weights for the classification instances, that are linked to the chance of that instance getting picked for the evaluation process and are determined by that instance's classification rate. These instance weights change based on the misclassification rate of the instance. We thoroughly describe and experiment with the lazy evaluation on standard classification benchmark datasets and show that not only lazy evaluation approach uses less time to evolve the good solution, but can even produce statistically better solution due to changing instance weights and thus preventing the overfitting of the solutions.
机译：在使用遗传程序创建分类决策树的标准过程中，评估过程是整个进化循环中最耗时的部分。在这里，我们介绍了进化过程中分类决策树的一种惰性评估方法，该方法不评估整个群体，而仅评估被选择参加锦标赛选择方法的个人。进一步，我们对分类实例使用了动态权重，动态权重与该实例被选择进行评估过程的机会有关，并由该实例的分类率来确定。这些实例权重根据实例的错误分类率而变化。我们对标准分类基准数据集的惰性评估进行了彻底的描述和实验，结果表明，不仅惰性评估方法花费更少的时间来演化良好的解决方案，而且由于实例权重的变化，甚至可以产生统计上更好的解决方案，从而避免了对模型的过度拟合。解决方案。

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