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)
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