摘要翻译：
我们讨论了如何使用遗传调控网络作为进化表示来解决一个典型的GP强化问题，极点平衡。该网络是几年前提出的人工调节网络的改进版本，只有找到一种将输入和输出连接到网络的适当方式才能解决这一任务。我们证明了该表示法能够很好地在问题域上推广，并讨论了这类模型的性能。
---
英文标题：
《Evolving Genes to Balance a Pole》
---
作者：
Miguel Nicolau (INRIA Saclay - Ile de France, LRI), Marc Schoenauer
  (INRIA Saclay - Ile de France, LRI), W. Banzhaf
---
最新提交年份：
2010
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  We discuss how to use a Genetic Regulatory Network as an evolutionary representation to solve a typical GP reinforcement problem, the pole balancing. The network is a modified version of an Artificial Regulatory Network proposed a few years ago, and the task could be solved only by finding a proper way of connecting inputs and outputs to the network. We show that the representation is able to generalize well over the problem domain, and discuss the performance of different models of this kind.
---
PDF链接：
https://arxiv.org/pdf/1005.2815