Partially Evaluated Genetic Algorithm Based on Fuzzy c-Means Algorithm
To find the optimal solution with genetic algorithm, it is desirable to maintain the population size as large as possible. In some cases, however, the cost to evaluate each individual is relatively high and it is difficult to maintain large population. To solve this problem we propose a partially evaluated GA based on fuzzy clustering, which considerably reduces evaluation cost without any loss of its performance by evaluating only one representative for each cluster. The fitness values of other individuals are estimated from the representative fitness values indirectly. We have used fuzzy c-means algorithm and distributed the fitness according to membership matrix. The results with nine benchmark functions are compared to six hard clustering algorithms with Euclidean distance and Pearson correlation coefficients for measuring the similarity between the representative and its members in fitness distribution.

title: Evolutionary Learning of Modular Neural Networks with Genetic Programming
abstract: Evolutionary design of neural networks has shown a great potential as a powerful optimization tool. However, most evolutionary neural networks have not taken advantage of the fact that they can evolve from modules. This paper presents a hybrid method of modular neural networks and genetic programming as a promising model for evolutionary learning. This paper describes the concepts and methodologies for the evolvable model of modular neural networks, which might not only develop new functionality spontaneously, but also grow and evolve its own structure autonomously. We show the potential of the method by applying an evolved modular network to a visual categorization task with handwritten digits. Sophisticated network architectures as well as functional subsystems emerge from an initial set of randomly-connected networks. Moreover, the evolved neural network has reproduced some of the characteristics of natural visual system, such as the organization of coarse and fine processing of stimuli in separate pathways.

title: Modular neural networks evolved by genetic programming
abstract: We present an evolvable model of modular neural networks which are rich in autonomy and creativity. In order to build an artificial neural network which is rich in autonomy and creativity, we have adopted the ideas and methodologies of artificial life. The paper describes the concepts and methodologies for the evolvable model of modular neural networks, which will be able not only to develop new functionality spontaneously but also to grow and evolve its own structure autonomously. Although the ultimate goal of this model is to design the control system for such behavior based robots as Khepera, we have attempted to apply the mechanism to a visual categorization task with handwritten digits. The evolutionary mechanism has shown a strong possibility to generate useful network architectures from an initial set of randomly connected networks.

title: Specification and validation of dynamic systems using temporal logic
abstract: A specification and validation technique for dynamic systems is proposed. In particular, a new temporal logic called HDTL, is presented and the tableau method revised for automatic analysis. Using a freeze quantifier, HDTL with the revised tableau method makes it possible to specify the correctness requirements of dynamic systems and validate them. The proposed logic is rather generic, i.e. it has only a few assumptions on operational language. The authors introduce a simple dynamic modelling language and illustrate its experiment. The experiment shows that HDTL is suitable for specifying dynamic properties and the analysis technique is promising.

title: TCP Performance Improvement with ACK Pacing in Wireless Data Networks
abstract: Wireless data services are fundamental in ubiquitous networks. In wireless data networks, the TCP congestion mechanism can mistake a wireless channel error or blackout for congestion on the route. Several wireless TCP protocols have been proposed considering TCP performance degradation in wireless systems. However, these protocols are too complex to deploy in wireless systems. In this paper, we propose the ACK Pacing algorithm for long lasting loss periods like a hand-off or a blackout. By pacing ACK packets sending, we prevent bursty data delivery to the old path and a spurious timeout occurrence. Also, paced ACK packets trigger the route update to the new path faster. The proposed algorithm can be implemented in the wireless-node-side which is easily applicable to wireless systems. From simulation results, ACK Pacing improves TCP performance compared with many TCP variations. The ACK Pacing algorithm shows good performance during a long lasting loss period.

title: Co-Validation Environment for Memory Card.

title: A Unified Route Optimization Scheme for Network Mobility
abstract: Recently Network Mobility(NEMO) is being concerned as new mobility issue. Lots of NEMO issues are already being touched in IETF NEMO WG but the solution is still premature especially to the Route Optimization (RO). NEMO has several problem spaces that need RO such as nested tunnels problem. Unfortunately, there is no solution that can be universally applied as one for all that results in supporting the coherent network mobility. In this paper, we propose a unified route optimization scheme that can solve several types of RO problem by using Path Control Header (PCH). In our scheme, Home Agent (HA) does piggyback the PCH on the packet which is reversely forwarded from Mobile Router (MR). That enables any PCH-aware routing facility on the route to make a RO tunnel with MR using the Care-of address of MR contained in the PCH. By applying to some already known NEMO RO problems, we show that our scheme can incrementally optimize the routes via default HA-MR tunnel through the simple PCH interpretation.

title: EIMD: A New Congestion Control for Fast Long-Distance Networks
abstract: In fast long-distance networks, TCP fails to fully utilize the bandwidth due to its congestion control mechanism A plethora of congestion control schemes that may enhance the performance of the transport protocol in fast long-distance networks have been proposed The proposed schemes aim to satisfy three requirements of congestion control schemes: bandwidth scalability, TCP friendliness, and RTT fairness However, due to the trade-off among these requirements, it is difficult to satisfy all the requirements simultaneously In this paper, we propose a new window-based congestion control scheme called EIMD (Exponential Increase/Multiplicative Decrease) that increases congestion window size exponentially to quickly grap the unutilized bandwidth We evaluate the performance of EIMD via computer simulations The simulation results show that EIMD satisfies the three requirements In addition, EIMD converges fast to the fair-share points.