This paper proposes a method to improve genetic programming with multiple trees (GP CN ). An individual in GP CN comprises multiple trees, and each tree has a number P that indicates the number of repetitive actions based on the tree. In previous work, a method for updating the number P has been proposed to obtain P suitable to the tree in evolution. However, in the method efficiency becomes worse as the range of P becomes wider. In order to solve the problem, in this study, two methods are proposed: inheriting the number P of a tree from an excellent individual and using mutation for preventing the number P from being into a local optimum. Additionally, a method to eliminate trees consisting of a single terminal node is proposed.

Recent studies have reported that population dynamics and evolutionary dynamics, occurring at different time scales, can be affected by each other. Our purpose is to explore the interaction between population and evolutionary dynamics using an artificial life approach based on a 3D physically simulated environment in the context of predator-prey and morphology-behavior coevolution. The morphologies and behaviors of virtual prey creatures are evolved using a genetic algorithm based on the predation interactions between predators and prey. Both population sizes are also changed, depending on the fitness. We observe two types of cyclic behaviors, corresponding to short-term and long-term dynamics. The former can be interpreted as a simple population dynamics of Lotka-Volterra type. It is shown that the latter cycle is based on the interaction between the changes in the prey strategy against predators and the long-term change in both population sizes, resulting partly from a tradeoff between their defensive success and the cost of defense.

Recent studies have reported that population dynamics and evolutionary dynamics, occurring at the different time scales, can be affected by each other. Our purpose is to explore the interaction between population and evolutionary dynam-ics using an Artificial Life approach based on a 3D physically simulated environment in the context of the predator-prey and morphology-behavior coevolution. The morphologies and behaviors of virtual prey creatures are evolved using a ge-netic algorithm based on the predation interactions between predators and prey. Both population sizes are also changed depending on the fitness. We see in the evolutionary sim-ulations two types of cyclic behaviors corresponding to the short-term and long-term dynamics. The former can be in-terpreted as a simple population dynamics of Lotka-Volterra type models. It is shown that the latter cycle is based on the interaction between the changes in the prey strategy against predators and the long-term change in both population sizes, resulting partly from a tradeoff between their defensive suc-cess and the cost of defense.

This paper proposes Genetic Programming(GP) with control nodes using the conditional probability and the island model for efficient learning of agent behavior. In the methods, each individual has a chromosome representing agent behavior as several trees. In GP using the conditional probability, individuals with high fitness values are used to produce conditional probability tables to generate individuals in the next generation. In GP using the island model, the population is divided into two islands of individuals: one island keeps diversity of individuals and the other puts emphasis on the accuracy of the solution. The methods are applied to a garbage collection problem and Santa Fe Trail problem. The proposed methods are compared with traditional GP, GP with control nodes, and Genetic Network Programming(GNP) with control nodes. Experimental results show that the proposed methods are efficient.

We present the results of evolving articulated virtual creature foraging in a 3D physically simulated environment filled with stationary food objects. Simple block creatures with sigmoidal neural networks are evolved through a genetic algorithm using a fitness function based on the consumption amount. The results show the evolution of successful foraging behaviors performing well in environments with various food distributions. We analyze the foraging based on its efficiency, creature morphologies, movement strategies, and the food density and entropy in the simulation environment.

This paper proposes a new SVM based technique for combining signature verification techniques using off-line features and on-line features. The off-line feature based technique employs gradient feature vector representing the shape of signature image, and the on-line feature based technique employs dynamic programming (DP) matching technique for time series data of the signatures. The final decision (verification) is performed by SVM based on output from those off-line and online techniques. In the evaluation test the proposed technique achieved 92.96% verification accuracy, which is 1.4% higher than the better accuracy obtained by the individual techniques. This result shows that combining multiple techniques by SVM improves signature verification accuracy significantly.

This paper describes how to measure an eye diagram by using ATE (Automated Test Equipment) digital channel and to do a correlation with an oscilloscope for over-Giga-bps class high speed digital interfaces. The novel method named RNA (Recovering aNAlysis) is a post processing method to perform “phase tracking” in the eye diagram measurement on an ATE which does not have a CDR (Clock Data Recovery) hardware integrated. The RNA is an enhancement of the method named DNA (Data aNAlysis) that constructs an eye diagram by coherent waveform reconstruction with utilizing an ATE digital channel. The DNA is an elegant method to reconstruct digital signal waveform and its eye diagram; however it is not immune to slow jitter or wander of signal. In recent high speed digital interfaces, jitter tolerance is very critical so that the DNA is insufficient for coping with test devices containing slow jitter. The RNA is an enhanced DNA by implementing software quasi-CDR described in this paper. It significantly expands the application coverage. The eye diagram processed by this method is improved and good for parametric measurement such as rise time/fall time tests. Especially, it allows to do an easy correlation to the eye measured by an oscilloscope. Therefore the transition from bench systems to ATE for production test becomes smooth and efficient.

A tailbiting block-interleaved pipelining (TB-BIP) is proposed for deeply-pipelined turbo decoders. Conventional sliding window block-interleaved pipelining (SW-BIP) turbo decoders suffer from many warm-up calculations when the number of pipeline stages is increased. However, by using TB-BIP, more than 50% of the warm-up calculations are reduced as compared to SW-BIP. We have implemented a TB-BIP WiMAX turbo decoder with four pipeline stages in the area of 3.8 mm2using a 0.18 µm CMOS technology. The chip achieved 45 Mbps/iter and 3.11 nJ/b/iter at 99 MHz operation.

MOTIVATION: In contrast with conventional PCR using a pair of specific primers, some applications utilize a single unique primer in combination with a common primer, thereby relying solely on the former for specificity. These applications include rapid amplification of cDNA ends (RACE), adaptor-tagged competitive PCR (ATAC-PCR), PCR-mediated genome walking and so forth. Since the primers designed by conventional methods often fail to work in these applications, an improved strategy is required, particularly, for a large-scale analysis. RESULTS: Based on the structure of 'off-target' products in the ATAC-PCR, we reasoned that the practical determinant of the specificity of primers may not be the uniqueness of entire sequence but that of the shortest 3'-end subsequence that exceeds a threshold of duplex stability. We termed such a subsequence as a 'specificity-determining subsequence' (SDSS) and developed a simple algorithm to predict the performance of the primer: the algorithm identifies the SDSS of each primer and examines its uniqueness in the target genome. The primers designed using this algorithm worked much better than those designed using a conventional method in both ATAC-PCR and 5'-RACE experiments. Thus, the algorithm will be generally useful for improving various PCR-based applications.

MOTIVATION: Protein-protein interactions are systematically examined using the yeast two-hybrid method. Consequently, a lot of protein-protein interaction data are currently being accumulated. Nevertheless, general information or knowledge on protein-protein interactions is poorly extracted from these data. Thus we have been trying to extract the knowledge from the protein-protein interaction data using data mining. RESULTS: A data mining method is proposed to discover association rules related to protein-protein interactions. To evaluate the detected rules by the method, a new scoring measure of the rules is introduced. The method allowed us to detect popular interaction rules such as "An SH3 domain binds to a proline-rich region." These results indicate that the method may detect novel knowledge on protein-protein interactions.