MONSTER — The Ghost in the Connection Machine: Modularity of Neural Systems in Theoretical Evolutionary Research

Nigel Snoad, T. Bossomaier
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引用次数: 8

Abstract

Both genetic algorithms (GAs) and artificial neural networks (ANNs) (connectionist learning models) are effective generalisations of successful biological techniques to the artificial realm. Both techniques are inherently parallel and seem ideal for implementation on the current generation of parallel supercomputers. We consider how the two techniques complement each other and how combining them (i.e. evolving artificial neural networks with a genetic algorithm), may give insights into the evolution of structure and modularity in biological brains. The incorporation of evolutionary and modularity concepts into artificial systems has the potential to decrease the development time of ANNs for specific image and information processing applications. General considerations when genetically encoding ANNs are discussed, and a new encoding method developed, which has the potential to simplify the generation of complex modular networks. The implementation of this technique on a CM-5 parallel supercomputer raises many practical and theoretical questions in the application and use of evolutionary models with artificial neural networks.
怪物& # 8212;连接机器中的幽灵:理论进化研究中神经系统的模块化
遗传算法(GAs)和人工神经网络(ANNs)(连接主义学习模型)都是成功的生物技术在人工领域的有效推广。这两种技术本质上都是并行的,似乎非常适合在当前一代并行超级计算机上实现。我们考虑这两种技术如何相互补充,以及如何将它们结合起来(即进化人工神经网络与遗传算法),可以深入了解生物大脑的结构和模块化的进化。将进化和模块化概念结合到人工系统中有可能减少针对特定图像和信息处理应用的人工神经网络的开发时间。讨论了遗传编码人工神经网络时的一般考虑因素,并开发了一种新的编码方法,该方法有可能简化复杂模块化网络的生成。该技术在CM-5并行超级计算机上的实现,为人工神经网络进化模型的应用和使用提出了许多实际和理论问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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