(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 10, 2018 252 | Page www.ijacsa.thesai.org Convolutional Neural Network Hyper-Parameters Optimization based on Genetic Algorithms Sehla Loussaief 1 , Afef Abdelkrim 2 Laboratory of Research in Automatic (LA.R.A) National Engineering School of Tunis (ENIT), University of Tunis El Manar National Engineering School of Carthage (ENICarthage), University of Carthage Tunis, Tunisia Abstract—In machine learning for computer vision based applications, Convolutional Neural Network (CNN) is the most widely used technique for image classification. Despite these deep neural networks efficiency, choosing their optimal architecture for a given task remains an open problem. In fact, CNNs performance depends on many hyper-parameters namely CNN depth, convolutional layer number, filters number and their respective sizes. Many CNN structures have been manually designed by researchers and then evaluated to verify their efficiency. In this paper, our contribution is to propose an innovative approach, labeled Enhanced Elite CNN Model Propagation (Enhanced E-CNN-MP), to automatically learn the optimal structure of a CNN. To traverse the large search space of candidate solutions our approach is based on Genetic Algorithms (GA). These meta-heuristic algorithms are well- known for non-deterministic problem resolution. Simulations demonstrate the ability of the designed approach to compute optimal CNN hyper-parameters in a given classification task. Classification accuracy of the designed CNN based on Enhanced E-CNN-MP method, exceed that of public CNN even with the use of the Transfer Learning technique. Our contribution advances the current state by offering to scientists, regardless of their field of research, the ability of designing optimal CNNs for any particular classification problem. Keywords—Machine learning; computer vision; image classification; convolutional neural network; CNN hyper parameters; enhanced E-CNN-MP; genetic algorithms; learning accuracy I. INTRODUCTION Image classification is an important task in computer vision involving a large area of applications such as object detection, localization and image segmentation [1-3]. The most adopted methods for image classification are based on deep neural network and especially Convolutional Neural Networks (CNN). These deep networks have demonstrated impressive and sometimes human-competitive results [4,5]. CNN deep architecture can be divided in two main parts [6]. The first part, based on convolutional layers CNN, offers the ability of features extraction and input image encoding. Whereas, the second one is a fully connected neural network classifier which role is to generate a prediction model for the classification task. A CNN model is described by many hyper-parameters specifically convolutional layers number, filters number and their respective sizes, etc. Many researchers proposed different CNN models such as AlexNet, Znet, etc. To improve the network accuracy some of them choose to increase the depth of the network [7]. Others propose new internal configurations [8]. Although, these state- of-the-art CNNs have been shown to be efficient, many of them were manually designed. During our research, we note that a miss configured values of CNN hyper-parameters namely the network depth, the number of filters and their respective sizes dramatically affect the performance of the classifier. In addition, manually, enumerating all the use cases and selecting optimal values for these hyper-parameters is almost impossible even with a fixed number of convolutional layers. Through contributions held in this paper we propose an innovative approach, labeled Enhanced Elite CNN Model propagation (Enhanced E-CNN MP), to automatically learn optimal CNN hyper-parameters values leading to a best CNN structure for a particular classification problem. Our approach is based on Genetic Algorithms (GA) known to be meta heuristic methods for non- deterministic problem resolution. Each CNN candidate solution structure, is encoded as an individual (chromosome). To search for the best fit individual, the proposed method is based on “The elite propagation” through the whole GA process. The designed Enhanced E-CNN MP approach is an innovative approach. Our contribution will allow scientists to design their own CNN based prediction model suitable for their particular image classification problem. This paper is organized as follows. Section II provides an overview of Convolutional Neural Network. In section III, the Genetic Algorithms paradigm is exposed. Problem statement is presented through section IV. Section V introduces related work. Section VI illustrates the designed Elite CNN Model Propagation (E-CNN-MP) approach based on GAs for CNN hyper parameters optimization. E-CNN-MP simulations and results are presented in section VII. Through section VIII, an Enhanced E-CNN-MP version is proposed. The last section includes our concluding remarks. II. DEEP LEARNING BASED ON CONVOLUTIONAL NEURAL NETWORK A neural network is a mathematical model with a design inspired from biological neurons. This network architecture is divided in layers. Each layer is a set of neurons. The first layer of a neural network is the input layer into which we inject the