Regularization approach to inductive genetic programming
Regularization approach to inductive genetic programming
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DOI：
10.1109/4235.942530
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发表时间：
2001-08-01
影响因子：
14.3
通讯作者：
Iba, H
中科院分区：
文献类型：
--
作者：
Nikolaev, NY;Iba, H
本文提出了一种用于学习多项式的归纳遗传规划的正则化方法。我们的目标是实现最佳的进化性能时，搜索表示为树结构的高阶多元多项式。我们展示了如何通过平衡其统计偏差与方差来改进多项式的遗传规划。通过在树节点中采用一组基多项式来减少偏差，以更好地与示例一致。由于这通常会导致过度拟合，因此通过对适应度函数进行正则化来降低方差可以抵消这种趋势。我们证明，这种平衡有利于搜索，以及使发现的吝啬，准确和预测的多项式。实验结果表明，这种正则化方法优于传统的遗传编程的基准数据挖掘和实际的时间序列预测任务。
This paper presents an approach to regularization of inductive genetic programming tuned for learning polynomials. The objective is to achieve optimal evolutionary performance when searching high-order multivariate polynomials represented as tree structures. We show how to improve the genetic programming of polynomials by balancing its statistical bias with its variance. Bias reduction is achieved by employing a set of basis polynomials in the tree nodes for better agreement with the examples. Since this often leads to overfitting, such tendencies are counteracted by decreasing the variance through regularization of the fitness function. We demonstrate that this balance facilitates the search as well as enables discovery of parsimonious, accurate, and predictive polynomials. The presented experimental results show that this regularization approach outperforms traditional genetic programming on benchmark data mining and practical time-series prediction tasks.