A comparison of fitness-case sampling methods for genetic programming
A comparison of fitness-case sampling methods for genetic programming
复制标题
遗传规划的适应度案例抽样方法的比较
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Uriel López
中科院分区：
文献类型：
--
作者：
Yuliana Martínez;Enrique Naredo;L. Trujillo;P. Legrand;Uriel López
摘要遗传程序设计（GP）是一种用于程序自动归纳的进化计算范式。GP已经产生了令人印象深刻的结果，但它仍然需要克服一些实际的限制，特别是它的高计算成本，过度拟合和过度的代码增长。最近，许多研究人员提出了适合情况下的抽样方法，以克服这些问题，在几个有限的测试混合的结果。本文对四种适合度抽样方法进行了广泛的比较研究，即：交错抽样、随机交错抽样、词典选择和保持最差交错抽样。在11个符号回归问题和11个监督分类问题上，使用10个合成基准和12个真实数据集对算法进行了比较。它们是基于测试性能，过拟合和平均程序大小进行评估，并与标准GP搜索进行比较。使用非参数多组检验和事后成对统计检验进行比较。实验结果表明，适合情况下采样方法是特别有用的困难的现实世界的符号回归问题，提高性能，减少过拟合和限制代码增长。另一方面，当考虑监督二进制分类时，似乎适应度案例采样不能改善GP性能。
Abstract Genetic programming (GP) is an evolutionary computation paradigm for automatic program induction. GP has produced impressive results but it still needs to overcome some practical limitations, particularly its high computational cost, overfitting and excessive code growth. Recently, many researchers have proposed fitness-case sampling methods to overcome some of these problems, with mixed results in several limited tests. This paper presents an extensive comparative study of four fitness-case sampling methods, namely: Interleaved Sampling, Random Interleaved Sampling, Lexicase Selection and Keep-Worst Interleaved Sampling. The algorithms are compared on 11 symbolic regression problems and 11 supervised classification problems, using 10 synthetic benchmarks and 12 real-world data-sets. They are evaluated based on test performance, overfitting and average program size, comparing them with a standard GP search. Comparisons are carried out using non-parametric multigroup tests and post hoc pairwise statistical tests. The experimental results suggest that fitness-case sampling methods are particularly useful for difficult real-world symbolic regression problems, improving performance, reducing overfitting and limiting code growth. On the other hand, it seems that fitness-case sampling cannot improve upon GP performance when considering supervised binary classification.