The paper "Evolving Deep Neural Networks" by Risto Miikkulainen et al. proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. The method extends existing neuroevolution techniques to include topology, components, and hyperparameters, achieving results comparable to human-designed architectures in standard benchmarks such as object recognition and language modeling. The approach is also applied to a real-world application of automated image captioning on a magazine website. The authors demonstrate that CoDeepNEAT can discover complex and effective network structures, and with anticipated increases in computing power, the method is expected to surpass human design in the future. The paper discusses the background of neuroevolution techniques, the evolution of DNNs, and the application of CoDeepNEAT to convolutional and LSTM networks, as well as a case study on image captioning for the blind. The results show that the evolutionary approach is feasible and can lead to practical applications in various domains.The paper "Evolving Deep Neural Networks" by Risto Miikkulainen et al. proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. The method extends existing neuroevolution techniques to include topology, components, and hyperparameters, achieving results comparable to human-designed architectures in standard benchmarks such as object recognition and language modeling. The approach is also applied to a real-world application of automated image captioning on a magazine website. The authors demonstrate that CoDeepNEAT can discover complex and effective network structures, and with anticipated increases in computing power, the method is expected to surpass human design in the future. The paper discusses the background of neuroevolution techniques, the evolution of DNNs, and the application of CoDeepNEAT to convolutional and LSTM networks, as well as a case study on image captioning for the blind. The results show that the evolutionary approach is feasible and can lead to practical applications in various domains.