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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Spectrum-Diverse Neuroevolution With Unified Neural Models
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Spectrum-Diverse Neuroevolution With Unified Neural Models

机译：统一神经模型的频谱多样化神经进化

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摘要

Learning algorithms are being increasingly adopted in various applications. However, further expansion will require methods that work more automatically. To enable this level of automation, a more powerful solution representation is needed. However, by increasing the representation complexity, a second problem arises. The search space becomes huge, and therefore, an associated scalable and efficient searching algorithm is also required. To solve both the problems, first a powerful representation is proposed that unifies most of the neural networks features from the literature into one representation. Second, a new diversity preserving method called spectrum diversity is created based on the new concept of chromosome spectrum that creates a spectrum out of the characteristics and frequency of alleles in a chromosome. The combination of spectrum diversity with a unified neuron representation enables the algorithm to either surpass or equal NeuroEvolution of Augmenting Topologies on all of the five classes of problems tested. Ablation tests justify the good results, showing the importance of added new features in the unified neuron representation. Part of the success is attributed to the novelty-focused evolution and good scalability with a chromosome size provided by spectrum diversity. Thus, this paper sheds light on a new representation and diversity preserving mechanism that should impact algorithms and applications to come.
机译：学习算法正在各种应用中被越来越多地采用。但是，进一步扩展将需要自动运行的方法。为了实现这种水平的自动化，需要更强大的解决方案表示。然而，通过增加表示复杂度，出现了第二个问题。搜索空间变得巨大，因此，还需要相关的可扩展且高效的搜索算法。为了解决这两个问题，首先提出了一种强大的表示形式，它将文献中的大多数神经网络特征统一为一个表示形式。其次，基于染色体光谱的新概念创建了一种称为光谱多样性的新的多样性保存方法，该新概念根据染色体中等位基因的特征和频率创建了一个光谱。频谱多样性与统一的神经元表示相结合，使该算法可以在所测试的所有五类问题上均达到或等于增强拓扑的NeuroEvolution。消融测试证明了良好的结果，表明在统一神经元表示中添加新功能的重要性。成功的一部分归因于以新颖性为中心的进化和良好的可扩展性，以及由频谱多样性提供的染色体大小。因此，本文阐明了一种新的表示形式和多样性保留机制，该机制应影响以后的算法和应用。

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