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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction
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Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction

机译：Elman递归神经网络协同协作进化中的竞争与协作，用于时间序列预测

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摘要

Collaboration enables weak species to survive in an environment where different species compete for limited resources. Cooperative coevolution (CC) is a nature-inspired optimization method that divides a problem into subcomponents and evolves them while genetically isolating them. Problem decomposition is an important aspect in using CC for neuroevolution. CC employs different problem decomposition methods to decompose the neural network training problem into subcomponents. Different problem decomposition methods have features that are helpful at different stages in the evolutionary process. Adaptation, collaboration, and competition are needed for CC, as multiple subpopulations are used to represent the problem. It is important to add collaboration and competition in CC. This paper presents a competitive CC method for training recurrent neural networks for chaotic time-series prediction. Two different instances of the competitive method are proposed that employs different problem decomposition methods to enforce island-based competition. The results show improvement in the performance of the proposed methods in most cases when compared with standalone CC and other methods from the literature.
机译：通过协作，弱种可以在不同物种争夺有限资源的环境中生存。合作协同进化（Cooperative coevolution，CC）是一种自然启发式的优化方法，可以将问题分解为多个子组件，并通过遗传分离它们来演化它们。问题分解是使用CC进行神经进化的重要方面。 CC采用不同的问题分解方法将神经网络训练问题分解为子组件。不同的问题分解方法具有在进化过程中不同阶段有用的功能。 CC需要适应性，协作性和竞争性，因为使用多个子群体来代表问题。在CC中增加协作和竞争非常重要。本文提出了一种竞争性CC方法，用于训练递归神经网络进行混沌时间序列预测。提出了两种不同的竞争方法实例，它们采用不同的问题分解方法来实施基于岛屿的竞争。结果表明，与独立CC和文献中的其他方法相比，在大多数情况下，所提出方法的性能有所提高。

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