用结构目标指导神经进化
Guiding Neuroevolution with Structural Objectives
摘要：
神经网络的结构和性能是密切相关的，并且通过使用进化算法，可以探索最佳地适应于给定任务的神经网络结构。用与网络结构相关的额外目标指导这种神经进化已经被证明在某些情况下可以提高性能，特别是当模块化神经网络是有益的时候。然而，除了旨在使网络更加模块化的目标之外，这种结构性目标尚未得到广泛探讨。我们提出了两个新的结构目标，并测试他们的能力，指导不断发展的神经网络的两个问题，可以受益于分解成子任务。第一个结构目标指导进化，使神经网络与用户推荐的分解模式对齐。直观地说，这应该是一个强大的指导目标的问题，人类用户可以很容易地识别一个结构。第二个结构性目标引导向分解模式具有高度多样性的种群进化。这导致探索许多不同的方法来分解问题，允许进化更快地找到好的分解。我们的目标问题的测试表明，这两种方法表现良好的问题具有非常清晰和可分解的结构。然而，在最佳分解不太明显的问题上，结构多样性目标被发现优于其他结构目标-这种技术甚至可以在没有任何可分解结构的问题上提高性能。
英文摘要：
The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be explored. Guiding such neuroevolution with additional objectives related to network structure has been shown to improve performance in some cases, especially when modular neural networks are beneficial. However, apart from objectives aiming to make networks more modular, such structural objectives have not been widely explored. We propose two new structural objectives and test their ability to guide evolving neural networks on two problems which can benefit from decomposition into subtasks. The first structural objective guides evolution to align neural networks with a user-recommended decomposition pattern. Intuitively, this should be a powerful guiding target for problems where human users can easily identify a structure. The second structural objective guides evolution towards a population with a high diversity in decomposition patterns. This results in exploration of many different ways to decompose a problem, allowing evolution to find good decompositions faster. Tests on our target problems reveal that both methods perform well on a problem with a very clear and decomposable structure. However, on a problem where the optimal decomposition is less obvious, the structural diversity objective is found to outcompete other structural objectives -- and this technique can even increase performance on problems without any decomposable structure at all.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
