用遗传算法演化神经网络，揭示局部学习如何催生全局智能。
The Evolution of Learning Algorithms for Artificial Neural Networks
- 用遗传算法优化网络结构与学习规则
- 成功演化出能学全四个一元布尔函数的网络
- 展示分布式学习行为的涌现机制
本文研究一种权重根据局部学习规则更新的神经网络模型。为检验局部学习规则是否足以实现学习，我们通过遗传编码方式表示网络架构与学习动态，并施加选择压力，进化出能学习四个一元布尔函数的网络。分析表明，学习行为是整个网络的分布式属性。最后讨论了遗传算法作为发现工具的潜力。
原文摘要 · Abstract (English)
In this paper we investigate a neural network model in which weights between computational nodes are modified according to a local learning rule. To determine whether local learning rules are sufficient for learning, we encode the network architectures and learning dynamics genetically and then apply selection pressure to evolve networks capable of learning the four boolean functions of one variable. The successful networks are analysed and we show how learning behaviour emerges as a distributed property of the entire network. Finally the utility of genetic algorithms as a tool of discovery is discussed.
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