基于自然启发的Firefly反向传播算法分析 神经网络训练

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摘要翻译：
优化算法通常受到元启发式方法的影响。近年来，为了寻找更好的解，人们发展了几种混合优化方法。本文采用元启发式自然启发算法，并用反向传播方法训练前馈神经网络。Firefly算法是一种自然启发的元启发式算法，它与反向传播算法相结合，在训练前馈神经网络时获得了更快的收敛速度。在一些标准数据集上对所提出的技术进行了测试。结果表明，该方法在很少的迭代次数内收敛性得到了改善。并与基于反向传播的遗传算法进行了比较。结果表明，该方法收敛时间短，收敛速度快，采用了最小前馈神经网络设计。
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英文标题：
《Analysis of a Nature Inspired Firefly Algorithm based Back-propagation
  Neural Network Training》
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作者：
Sudarshan Nandy, Partha Pratim Sarkar and Achintya Das
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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英文摘要：
  Optimization algorithms are normally influenced by meta-heuristic approach. In recent years several hybrid methods for optimization are developed to find out a better solution. The proposed work using meta-heuristic Nature Inspired algorithm is applied with back-propagation method to train a feed-forward neural network. Firefly algorithm is a nature inspired meta-heuristic algorithm, and it is incorporated into back-propagation algorithm to achieve fast and improved convergence rate in training feed-forward neural network. The proposed technique is tested over some standard data set. It is found that proposed method produces an improved convergence within very few iteration. This performance is also analyzed and compared to genetic algorithm based back-propagation. It is observed that proposed method consumes less time to converge and providing improved convergence rate with minimum feed-forward neural network design.
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PDF链接：
https://arxiv.org/pdf/1206.5360

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关键词：Fire 算法分析 神经网络 神经网 FIR algorithm 速度 feed meta 混合