Evolving neural networks in environments with delayed rewards by a real-coded GA using the unimodal normal distribution crossover
Evolving neural networks in environments with delayed rewards by a real-coded GA using the unimodal normal distribution crossover
复制标题
使用单峰正态分布交叉通过实数编码 GA 在具有延迟奖励的环境中进化神经网络
DOI：
10.1109/cec.2000.870361
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
N. Ono
中科院分区：
文献类型：
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作者：
I. Ono;Miyuki Takahashi;N. Ono
神经进化（NE）是利用遗传算法（GAs）训练神经网络的一种强化学习技术，它可以让智能体在具有延迟奖励的环境中学习适当的策略，即从感觉输入到动作输出的映射，因此受到了广泛的关注。虽然到目前为止已经对神经网络系统进行了一些研究，但在使用神经网络训练神经网络时，还没有研究考虑到权参数之间的上位性。在函数优化中，参数间的上位性是导致函数难以优化的重要特征之一。为了成功地优化具有大量待确定参数的困难函数，必须考虑参数之间的上位性。在本文中，我们提出了一个基于单峰正态分布交叉（UNDX）的实数编码遗传算法的网元系统。该算法在参数间具有强上位性的函数优化方面表现出优异的性能。将基于UNDX的网元系统应用于一些比以往工作更难的基准问题。结果表明，当我们用NE系统训练神经网络处理困难任务时，应该考虑权重参数之间的上位性。
The Neuro-Evolution (NE), the training of neural networks with genetic algorithms (GAs), has received much attention as one of the reinforcement learning techniques that can let agents learn appropriate policies, i.e. mappings from sensory inputs to action outputs, in environments with delayed rewards. Although several studies on NE systems have been made so far, there are no studies that take account of epistasis among weight parameters in training neural networks with GAs. In function optimization, epistasis among parameters is one of the important features which make functions difficult to be optimized. Epistasis among parameters has to be considered in order to successfully optimize difficult functions with large number of parameters to be determined. In this paper, we present an NE system based on a real-coded GA using the Unimodal Normal Distribution Crossover (UNDX). The UNDX shows excellent performance in optimizing functions with strong epistasis among parameters. The NE system based on the UNDX are applied to some benchmark problems, which are more difficult than those used in previous work. The results suggest that epistasis among weight parameters should be considered when we train neural networks for difficult tasks by NE systems.