A research on optimization of cellular neural networks based on evolutionary computation

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We developed a method to optimize neural networks named "Artificial Cellular Neural Networks (ACNN)". ACNN is a kind of neural network in which each neural unit connects only with his neighboring units. Since we need not connect any units which are far away from each other, we can easily make LSIs of two-dimensional and three-dimensional ACNN, In this research, connection weight among neural units is restricted to one of the three values, +1, -1 and 0, and every connection weights in ACNNs can be set independently. Therefore, we have to set many connection weights adequately in order to make an ACNN work well for a given task. We employed Genetic Algorithm(GA) for this optimization. Using a GA, we can make ACNNs for a variety of information processing tasks through generation iterations from the initial population of ACNNs which have randomly set connection weights. We developed automatic designing methods for 2D and 3D ACNN based on GA.Using our method, we can generate any ACNNs b.y defining evaluation functions appropriate for given tasks. As applications of our method, we treated several problems in the field of agent based artificial intelligence, First, we applied 2D ACNN for chasing problems. In these problems, action control of a virtual chaser was controlled by a 2D ACNN, Next, we applied 3D ACNN for a maze problem. The input and output of a 3D ACNN in this problem are the current neighbor of an agent in a maze and his next moving direction, respectively. By using our method, a 3D ACNN which moves the agent from the start point to the goal point in the shortest time is automatically generated. We developed the optimization method for 2D and 3D ACNN and applied them to artificial intelligence. We are now planning to make LSI chips of 2D and 3D ACNN.