The most popular researches about Parallel GAs are implemented as; Population is devided into some subpopulations, each subpopulation executes GA independently and some individuals are migrated in fixed intervals or fixed probability. On the other hand, Grid Computing has been noticed and a research that implements Parallel GA by using Master-Worker model on Grid Computing has been reported. However, on the huge search space problems, Parallel GA by using Master-Worker model needs a lot of worker to get better solution quality. If there are a lot of workers, the traffic loads to the master.
In this paper, we propose Asynchronous Parallel Distributed GA by using Layered Server-Client model. This model is based on Elite Migration on Server-Client model we proposed before. In this model, an Elite Server manages some Subpopulation Clients, and a Master Server manages some Elite Servers. From this structure, the number of Subpopulation Clients that a Elite Server manages is able to be reduced and the traffic on an Elite Server is also able to be reduced. To evaluate our proposed model, we apply to some problems. As the results, we confirm that the fitness is as well as that of current methods and the traffic is less than that of current methods. We also confirm that the migration time is able to be reduced especially in large search space problems.

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This paper studies a procedure for identifying Hammerstein systems from input-output data, where the static nonlinearity is approximated by the trigonometric polynomials. Signals generated by the trigonometric function with white Gaussian inputs are analyzed in terms of the persistent excitation of linear time-invariant systems. Based on the analysis, an identification algorithm for Hammerstein systems is proposed via a subspace identification method. Numerical simulation results are also included to illustrate the effectiveness of the present algorithm.

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We propose an efficient state-space construction method for a reinforcement learning. Our method controls the number of categories with improving the clustering method of Fuzzy ART which is an autonomous state-space construction method. The proposed method represents weight vector as the mean value of input vectors in order to curb the number of new categories and eliminates categories whose state values are low to curb the total number of categories. As the state value is updated, the size of category becomes small to learn policy strictly. We verified the effectiveness of the proposed method with simulations of a reaching problem for a two-link robot arm. We confirmed that the number of categories was reduced and the agent achieved the complex task quickly.

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This paper presents control system synthesis of providing a balance between normal-case performance, safety and fault-case performance according to the international standard on safety, IEC 61508. It is based on multiobjective design for simultaneous problems for each context to optimize only normal-case performance out of the whole including fault-case performance.

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This paper investigates writer verification using feature parameters based on the knowledge of document examiners, which are automatically extracted from handwritten kanji characters on a digitizing tablet. Criteria of feature selection using the evaluation measure that is obtained by modifying the measure of decidability, d-prime, is established and the criteria are applied to the evaluation measures that are calculated from learning samples. Then two classifiers based on the frequency distribution of deviations of the selected features are proposed and its design method using learning samples is showed. The effectiveness of the proposed method is evaluated by verification experiments with the database including skilled forgeries. The experimental results show that the proposed methods are effective in writer verification.

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