It is thought that the main elements of commonsense judgment similar to human beings are the concept-base and the association mechanism based on the association between concepts. It is expected that the structure of the concept-base should be as simple as possible since the concept-base has to be expanded and refined by automated learning. The paper proposes a new method of measuring the degree of association between concepts. In the conventional method, a concept is expressed by first attributes vector model, and the degree of association between concepts is derived from an inner product of vectors. In this model, since each first attribute must be converted to its category, a category database such as a thesaurus is required. By the proposed method, the degree of association is derived using the chain of concepts without category. With experimental results using the concept-base which consists of about 40000 concepts, it is shown that the proposed method outputs a closer degree of association to that decided by human judgment than the conventional method.

Although GAs are widely used for optimization problems and often produce good results, there are also problems, such as premature convergence and evolutionary stagnation. The premature convergence caused by a reduction of the diversity and evolutionary stagnation in GAs are observed, and a new genetic algorithm, named multi-step GA (MSGA) is proposed. MSGA narrows the search space to avoid evolutionary stagnation and restarts from the initial population, keeping past results to avoid premature convergence. To evaluate MSGA, traveling salesman problems are considered. As a result, MSGA can avoid premature convergence and evolutionary stagnation and shows higher performance than other conventional GAs.

In a computer aided design (CAD) system, shape design or shape modeling is one often a bottleneck because shape design is a trial and error process often depending on a designer's intuition and experience. Authors have already presented a method, named evolutional shape modeling (ESM), to generate suitable shapes automatically using genetic algorithm (GA) and free-form deformation (FFD). This ESM method, however, has a limitation on its ability of shape generation because of the limitation on FFD's shape deformation ability and the limitation on standard GA's ability. This paper proposes a new evolutional shape modeling method. It includes new genetic algorithm, named multistep GA, which can avoid premature convergence, and new constructing method of FFD by which shape deformation ability is highly improved.

This correspondence describes an algorithm for generating a set of test items and a test sequence which can be used at a minimal cost for testing protocol implementations for a layered protocol. "Set" and "weight" concepts are introduced and "test cost" is defined as the weighted sum of directed arcs.