Bagging, Boosting, and bloating in Genetic Programming
Bagging, Boosting, and bloating in Genetic Programming
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遗传编程中的装袋、提升和膨胀
DOI：
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发表时间：
1999
期刊：
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通讯作者：
H. Iba
中科院分区：
文献类型：
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作者：
H. Iba
提出了一种基于重采样技术的遗传规划的扩展算法，即袋装和Boost算法。这两种方法都是为了改进学习算法而对训练数据进行操作。从理论上讲，它们可以通过重复运行任何弱学习算法来显著减少错误。本文对遗传算法进行了扩展，将整个种群划分为一组子种群，每个子种群通过袋化和Boosting方法进行进化。实验结果表明，该方法是有效的。从膨胀效应的角度，通过与传统GP的比较，对其性能进行了讨论。
We present an extension of GP (Genetic Programming) by means of resampling techniques, i.e., Bagging and Boosting. These methods both manipulate the training data in order to improve the learning algorithm. In theory they can significantly reduce the error of any weak learning algorithm by repeatedly running it. This paper extends GP by dividing a whole population into a set of sub-populations, each of which is evolvable by using the Bagging and Boosting methods. The effectiveness of our approach is shown by experiments. The performance is discussed by the comparison with the traditional GP in view of the bloating effect.
DOI：
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发表时间：
1992
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影响因子：
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作者：
J. Koza
通讯作者：
J. Koza