In this paper, we apply a mutation operation based on a multivariate Cauchy distribution to fast evolutionary programming and analyze its effect in terms of various function optimizations. The conventional fast evolutionary programming in-cooperates the univariate Cauchy mutation in order to overcome the slow convergence rate of the canonical Gaussian mutation. For a mutation of n variables, while the conventional method utilizes n independent random variables from a univariate Cauchy distribution, the proposed method adopts n mutually dependent random variables that satisfy a multivariate Cauchy distribution. The multivariate Cauchy distribution naturally has higher probabilities of generating random variables in inter-variable regions than the univariate Cauchy distribution due to the mutual dependence among variables. This implies that the multivariate Cauchy random variable enhances the search capability especially for a large number of correlated variables, and, as a result, is more appropriate for optimization schemes characterized by interdependence among variables. In this sense, the proposed mutation possesses the advantage of both the univariate Cauchy and Gaussian mutations. The proposed mutation is tested against various types of real-valued function optimizations. We empirically find that the proposed mutation outperformed the conventional Cauchy and Gaussian mutations in the optimization of functions having correlations among variables, whereas the conventional mutations showed better performance in functions of uncorrelated variables.

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.

Spatially coupled (SC) low-density parity-check (LDPC) codes are defined by bipartite graphs that are obtained by assembling prototype graphs. The combination and connection of prototype graphs are designated by specifying some parameters, and Kudekar et al. showed that BP threshold of the ensemble of SC LDPC codes agrees with MAP threshold of the ensemble of regular LDPC codes when those parameters are grown up so that the code length tends to infinity. When we design SC LDPC codes with practical code length, however, it is not clear how to set those parameters to enhance the performance of SC LDPC codes. In this paper, we provide the result of numerical experiments that suggest the dependence of error performance of SC LDPC codes over BEC on their design parameters.

LT codes are the first practical rateless codes whose reception overhead totally depends on the degree distribution adopted. The capability of LT codes with a particular degree distribution named robust soliton has been theoretically analyzed; it asymptotically approaches the optimum when the message length approaches infinity. However, real applications making use of LT codes have finite number of input symbols. It is quite important to refine degree distributions because there are distributions whose performance can exceed that of the robust soliton distribution for short message length. In this work, a practical framework that employs evolutionary algorithms is proposed to search for better degree distributions. Our experiments empirically prove that the proposed framework is robust and can customize degree distributions for LT codes with different message length. The decoding error probabilities of the distributions found in the experiments compare well with those of robust soliton distributions. The significant improvement of LT codes with the optimized degree distributions is demonstrated in the paper.

As a network evolves following initial deployment, its service functions remain diversified through the openness of the network functions. This indicates that appropriate simplification of the service functions is essential if the evolving network is to achieve the required scalability of service processing and service management. While the screening of service functions is basically performed by network users and the market, several service functions will be automatically simplified based on the growth of the evolving network. This paper verifies the simplification of service functions resulting from the evolution of the network itself. First, the principles that serve as the basis for simplifying the service functions are explained using several practical examples. Next, a simulation model is proposed to verify the simplification of service functions in terms of the priority control function for path routing and load balancing among multiple paths. From the results of the simulation, this study clarifies that the anticipated simplification of service functions is actually realizable and the service performance requirements can be reduced as the network evolves after deployment. When the simplification of service functions can improve network quality, it accelerates the evolution of the network and increases the operator's revenue.

In this letter, we present a low-complexity residual symbol timing offset (STO) estimation scheme in a long term evolution (LTE) downlink system. The proposed scheme is designed to estimate STO without a priori knowledge of cell-specific reference signals, which reduces the arithmetic complexity while maintaining a similar performance to the conventional algorithm.

Scheduling restriction is attracting much attention in LTE-Advanced as a technique to reduce the power consumption and network overheads in interference coordinated heterogeneous networks (HetNets). Such a network with inter-cell interference coordination (ICIC) provides two radio resources with different channel quality statistics. One of the resources is protected (unprotected) from inter-cell interference (hence, called protected (non-protected) resource) and has higher (lower) average channel quality. Without scheduling restriction, the channel quality feedback would be doubled to reflect the quality difference of the two resources. We present a simple scheduling restriction scheme that addresses the problem without significant performance degradation. Users with relatively larger (smaller) average channel quality difference between the two resources are scheduled in the protected (non-protected) resource only, and a boundary user, determined by a proportional fair resource allocation (PFRA) under simplified static channels, is scheduled on one of the two resources or both depending on PFRA. Having most users scheduled in only one of the resources, the power consumption and network overheads that would otherwise be required for the channel quality feedback on the other resource can be avoided. System level simulation of LTE-Advanced downlink shows that the performance degradation due to our scheduling restriction scheme is less than 2%, with the average feedback reduction of 40%.

Evolutionary stability has been discussed as a fundamental issue in single-criterion games. We extend evolutionarily and neutrally stable strategies to multicriteria games. Keeping in mind the fact that a payoff is given by a vector in multicriteria games, we provide several concepts which are coincident in single-criterion games based on partial vector orders of payoff vectors. We also investigate the hierarchical structure of our proposed evolutionarily and neutrally stable strategies. Shapley had introduced concepts such as strong and weak equilibria. We discuss the relationship between these equilibria and our proposed evolutionary stability.

We present an Interactive Tabu Search (ITS) algorithm to reduce the evaluation load of Interactive Evolutionary Computation (IEC) users. Most previous IEC studies used an evaluation interface that required users to provide evaluation values for all candidate solutions. However, user's burden with such an evaluation interface is large. Therefore, we propose ITS where users choose the favorite candidate solution from the presented candidate solutions. Tabu Search (TS) is recognized as an optimization technique. ITS evaluation is simpler than Interactive Genetic Algorithm (IGA) evaluation, in which users provide evaluation values for all candidate solutions. Therefore, ITS is effective for reducing user evaluation load. We evaluated the performance of our proposed ITS and a Normal IGA (NIGA), which is a conventional 10-stage evaluation, using a numerical simulation with an evaluation agent that imitates human preferences (Kansei). In addition, we implemented an ITS evaluation for a running-shoes-design system and examined its effectiveness through an experiment with real users. The simulation results showed that the evolution performance of ITS is better than that of NIGA. In addition, we conducted an evaluation experiment with 21 subjects in their 20 s to assess the effectiveness of these methods. The results showed that the satisfaction levels for the candidates generated by ITS and NIGA were approximately equal. Moreover, it was easier for test subjects to evaluate candidate solutions with ITS than with NIGA.

We evaluate the rich-club property of the Internet topology at the autonomous system (AS) level by comparing the Internet AS graphs of traceroute and BGP, and the synthetic graphs of PFP model. The results indicate that, for rich-club coefficient, PFP model can exactly match traceroute AS graphs in the early years around 2002, but it has significantly deviated from the grown AS graphs since about 2010.

The dynamic competition between two bounded rational mobile virtual network operators (MVNOs) in a duopoly spectrum market is investigated. A two stage game is employed to model the interaction of the MVNOs and the quality of service of the secondary users is taken into account. The evolutionary game theory is introduced to model the dynamic strategy selections of MVNOs. Using replicated dynamics, the proposed evolutionary game algorithm can converge to a unique evolutionary stable strategy. Simulation results verify that the proposed algorithm can make the MVNOs adaptively adjust the strategies to approximate optimal solution.

Nowadays, software development societies have given more precedence to Open Source Software (OSS). There is much research aimed at understanding the OSS society to sustain the OSS product. To lead an OSS project to a successful conclusion, researchers study how developers change source codes called patches in project repositories. In existing studies, we found an argument in the conventional patch acceptance detection procedure. It was so simplified that it omitted important cases from the analysis, and would lead researchers to wrong conclusions. In this research, we propose an algorithm to overcome the problem. To prove out our algorithm, we constructed a framework and conducted two case studies. As a result, we came to a new and interesting understanding of patch activities.

The periodogram scheme is a versatile method for large frequency offset estimation which requires preambles, in general, and only a few reference signals. However, the drawback of the conventional scheme is its inaccuracy in detecting the integer part of frequency offset which dominates the accuracy of frequency offset estimation. In order to increase the accuracy of the estimation, we directly interpolate a coefficient of the periodogram. The conventional periodogram and proposed schemes are compared in terms of the mean square error performance in the long term evolution system. The performance of the proposed method is compared with the conventional one and the simulation results indicate the better performance of the proposed one.

A conical area evolutionary algorithm (CAEA) is presented to further improve computational efficiencies of evolutionary algorithms for bi-objective optimization. CAEA partitions the objective space into a number of conical subregions and then solves a scalar subproblem in each subregion that uses a conical area indicator as its scalar objective. The local Pareto optimality of the solution with the minimal conical area in each subregion is proved. Experimental results on bi-objective problems have shown that CAEA offers a significantly higher computational efficiency than the multi-objective evolutionary algorithm based on decomposition (MOEA/D) while CAEA competes well with MOEA/D in terms of solution quality.

Execution performance is critical for large-scale and data-intensive workflows. This paper proposes DISWOP, a novel scheduling algorithm for data-intensive workflow optimizations; it consists of three main steps: workflow process generation, task & resource mapping, and task clustering. To evaluate the effectiveness and efficiency of DISWOP, a comparison evaluation of different workflows is conducted a prototype workflow platform. The results show that DISWOP can speed up execution performance by about 1.6-2.3 times depending on the task scale.

This paper presents fundamental concepts, overall process and recent research issues of Mining Software Repositories. The data sources such as source control systems, bug tracking systems or archived communications, data types and techniques used for general MSR problems are also presented. Finally, evaluation approaches, opportunities and challenge issues are given.

In this letter, we apply dynamic software updating to long-lived applications on the DDS middleware while minimizing service interruption and satisfying Quality of Service (QoS) requirements. We dynamically updated applications which run on a commercial DDS implementation to demonstrate the applicability of our approach to dynamic updating. The results show that our update system does not impose an undue performance overhead–all patches could be injected in less than 350 ms and the maximum CPU usage is less than 17%. In addition, the overhead on application throughput due to dynamic updates ranged from 0 to at most 8% and the deadline QoS of the application was satisfied while updating.

Software Defined Radio (SDR) techniques are expected to be among the key technologies of heterogeneous cognitive radio networks for realizing efficient and convenient wireless communications by providing multiple radio services to users and decreasing development costs. In this paper, in order to evaluate the feasibility of SDR modems, we study the amount of computing throughput of a recent wireless system and determine a suitable modem architecture. Firstly, the functions for which SDR techniques provide significant benefits are clarified. Secondly, the computing throughputs are measured under the assumption that a dynamically reconfigurable processor, FlexSwordTM, is employed. Finally, based on a consideration of timing charts, we propose the architecture of an SDR-based modem with FlexSword. The possibility of implementing several wireless systems is also considered.

In most existing warships combat simulation system, the tactics of a warship is manipulated by human operators. For this reason, the simulation results are restricted due to the capabilities of human operators. To deal with this, we have employed the genetic algorithm for supporting the evolutionary simulation environment. In which, the tactical decision by human operators is replaced by the human model with a rule-based chromosome for representing tactics so that the population of simulations are created and hundreds of simulation runs are continued on the basis of the genetic algorithm without any human intervention until finding emergent tactics which shows the best performance throughout the simulation. Several simulation tests demonstrate the techniques.

In existing systems of mobile routers, the frequency band is shared in uplinks from wireless terminals to mobile routers, and carrier sense multiple access with collision avoidance (CSMA/CA) is generally used as the medium access control protocol. To use the frequency band effectively, adaptive control is one promising approach. In this paper, a decentralized access control protocol in which mobile routers adaptively select the minimum contention window size is proposed. However, because of their mobility, which is one of the main difference between mobile routers and fixed access points, individual local area networks (LANs) consisting of the mobile routers and wireless terminals randomly interact with each other, and such random interactions can cause instability. To analyze the stability of the proposed control, evolutionary game theory is introduced because a system with random interactions between numerous decision-making entities can be analyzed by using evolutionary game theory. Using evolutionary game theory, the condition for existence of a convergence point is obtained. In addition, to implement the decentralized access control, a learning rule is proposed. In the proposed learning rule, each mobile router selects a strategy based on the result of past trials. From the simulation result, it is confirmed that the decentralized access control converges to a point closed to the stable state derived through evolutionary game theory.