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The Role of Nearly Neutral Mutations in the Evolution of Dynamical Neural Networks

机译：几乎中性突变在动态神经网络演化中的作用

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The evolution of continuous time recurrent neural networks is increasingly being employed to evolve nervous systems for autonomous agents. Nonetheless, the picture of populations engaged in hill-climbing rugged fitness landscapes poses a problem of becoming trapped on a local hilltop. Developments in evolutionary theory and molecular biology have pointed to the importance of selective neutrality. The neutral theory claims that the great majority of evolutionary changes are caused not by Darwinian selection but by random drift of selectively neutral or nearly neutral mutants. However, with a few exceptions neutrality has generally been ignored in artificial evolution. This paper addresses the distribution of fitness effects of new mutations when evolving dynamical systems and provides evidence of an improved evolutionary search process when incorporating nearly-neutral drift. This is one of the most fundamental problems in artificial evolution, because it lies at the heart of maintaining a constant-innovative property.
机译：连续时间递归神经网络的演化正越来越多地被用于演化用于自主主体的神经系统。然而，在崎hill不平的崎fitness不平的健身景观中爬山的人群的图片构成了被困在当地山顶上的问题。进化论和分子生物学的发展指出了选择性中立的重要性。中性理论认为，大多数进化变化不是由达尔文选择引起的，而是由选择性中性或近中性突变体的随机漂移引起的。但是，除了少数例外，在人工进化中，中性通常被忽略。本文探讨了进化动力系统时新突变的适应性效应分布，并提供了在纳入接近中性漂移时改进的进化搜索过程的证据。这是人工进化中最基本的问题之一，因为它是保持恒定创新性质的核心。

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