Implementation of evolutionary algorithms for deep architectures
Sreenivas Sremath Tirumala · 2014
Abstract. Deep learning is becoming an increasingly interesting and powerful machine learning method with successful applications in many domains, such as natural language processing, image recognition, and hand-written character recognition. Despite of its eminent success, lim-itations of traditional learning approach may still prevent deep learning from achieving a wide range of realistic learning tasks. Due to the flexi-bility and proven effectiveness of evolutionary learning techniques, they may therefore play a crucial role towards unleashing the full potential of deep learning in practice. Unfortunately, many researchers with a strong background on evolutionary computation are not fully aware of the state-of-the-art research on deep learning. To close this knowledge gap and to promote the research on evolutionary inspired deep learning techniques, this paper presents a comprehensive review of the latest deep architec-tures and surveys important evolutionary algorithms that can potentially be explored for training these deep architectures. Index terms — Deep Architectures, Deep Learning, Evolutionary Algorithms 1