1. Optimizing Deep Learning by Hyper Heuristic Approach for Classifying Good Quality Images 2. Muneeb Hassan, muneeb_hassan@outlook.com Nasser R. Sabar, nasser.sabar@gmail.com Andy Song, andy.song@rmit.edu.au 3. the name of the corresponding author: Andy Song, andy.song@rmit.edu.au 4. the abstract of the paper(s); Deep Convolutional Neural Network (CNN), which is one of the prominent deep learning methods, has shown a remarkable success in a variety of computer vision tasks, especially image classification. However, tuning CNN hyper-parameters requires expert knowledge and a large amount of manual effort of trial and error. In this work, we present the use of CNN on classifying good quality images versus bad quality images without understanding the image content. The well known data-sets were used for performance evaluation. More importantly we propose a hyper-heuristics approach on CNN for tuning its hyper-parameters. The proposed method encompasses of a high level strategy and various low level heuristics. The high level strategy utilises search performance to determine how to apply low level heuristics to automatically find an optimal set of CNN hyper-parameters. Our experiments show the effectiveness of this hyper-heuristic approach which can achieve high accuracy even when the training size is significantly reduced and conventional CNNs can no longer perform well. In short the proposed hyper-heuristic method does enhance CNN deep learning. 5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies; D 6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission); The main contribution is that the evolved parameter setting is better than the manually tuned setting for training the Convolutionary Neural Network for image classification. 7. a full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); 1. 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