Dynamical Genetic Programming in XCSF
Dynamical Genetic Programming in XCSF
复制标题
XCSF 中的动态遗传编程
DOI：
10.1162/evco_a_00080
复制
发表时间：
2013
影响因子：
6.8
通讯作者：
L. Bull
中科院分区：
文献类型：
--
作者：
R. Preen;L. Bull
已经提出了许多用于学习分类器系统的表示方案，从二进制编码到人工神经网络。本文介绍了从调查中使用的XCSF学习分类系统的时间动态符号表示的结果。特别是，动态算术网络被用来表示传统的条件-动作产生式系统规则，以解决连续值强化学习问题，并执行符号回归，在一些复合多项式任务上找到与传统遗传编程竞争的性能。此外，网络输出稍后以不同的时间间隔重复采样，以执行金融时间序列的多步预测。
A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.
DOI：
10.1007/bfb0055923
发表时间：
1998
期刊：
--
影响因子：
--
作者：
Moshe Sipper
通讯作者：
Moshe Sipper