In this paper, an autonomous ensemble approach of improved quantum inspired gravitational search algorithm (IQI-GSA) and hybrid deep neural networks (HDNN) is proposed for the optimization of computational problems. The IQI-GSA is a combinational variant of gravitational search algorithm (GSA) and quantum computing (QC). The improved variant enhances the diversity of mass collection for retaining the stochastic attributes and handling the local trapping of mass agents. Further, the hybrid deep neural network encompasses the convolutional and recurrent neural networks (HDCR-NN) which analyze the relational & temporal dependencies among the different computational components for optimization. The proposed ensemble approach is evaluated for the application of facial expression recognition by experimentation on Karolinska Directed Emotional Faces (KDEF) and Japanese Female Facial Expression (JAFFE) datasets. The experimentation evaluations evidently exhibit the outperformed recognition rate of the proposed ensemble approach in comparison with state-of-the-art techniques.

中文翻译：

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一种改进的量子启发式引力搜索算法和混合深度神经网络的集成方法用于计算优化

在本文中，提出了一种改进的量子启发引力搜索算法（IQI-GSA）和混合深度神经网络（HDNN）的自治集成方法，用于优化计算问题。IQI-GSA 是引力搜索算法 (GSA) 和量子计算 (QC) 的组合变体。改进的变体增强了质量收集的多样性，以保留随机属性并处理质量代理的局部捕获。此外，混合深度神经网络包括卷积和递归神经网络 (HDCR-NN)，它分析不同计算组件之间的关系和时间依赖性以进行优化。通过在 Karolinska 定向情绪面孔 (KDEF) 和日本女性面部表情 (JAFFE) 数据集上进行实验，评估了所提出的集成方法在面部表情识别中的应用。与最先进的技术相比，实验评估明显展示了所提出的集成方法的优于识别率。