Tadataka Konishi

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In this paper, we discuss on new Coevolutionary Genetic Algorithm for Constraint Satisfaction. Our basic idea is to explore effective genetic information in the population, i.e., schemata, and to exploit the genetic information in order to guide the population to better solutions. Our Coevolutiona,ry Genetic Algorithm (CGA) consists of two GA populations;(More)
In this paper, a method for measuring the shape of a columnar object with specular surfaces by a slit ray projection method is proposed. Although the slit ray projection method is effective for measuring the shape of an object having diffuse reflective characteristics, applying the method to an object with specular surfaces has hitherto been difficult. In(More)
In this paper, we discuss the adaptability of Coevolutionary Genetic Algorithms on dynamic environments. Our CGA consists of two populations: solution-level one and schema-level one. The solution-level population searches for the good solution in a given problem. The schema-level population searches for the good schemata in the former population. Our CGA(More)
The spurious minima in optimizing operation is one of the difficulty for Lyapunov function. In this paper, novel lossless image coding method based on lifting scheme using discrete-time cellular neural networks (DT-CNNs) with annealing approach is proposed. In the proposed, the image prediction of lifting scheme is implemented by DT-CNNs solving the(More)
This paper proposes a novel image resolution up-scaling method using discrete-time cellular neural network (DT-CNN) with multi-level quantization function for output of a cell. The nonlinear interpolative approximation capability of the DT-CNN is used to generate an resolution enhanced image from its low-resolution version. Our proposed method consists of(More)
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