Recent developments in the field of Evolutionary Computation have led to a renewed interest in its real-world applications on wide variety of application domains. This paper attempts to provide a comprehensive overview of recent advances of Evolutionary Computation studies. First, we describe the basic mechanisms of Evolutionary Computation. Moreover, a devlopment diagram of Evolutionary Computations is also shown in order to understand research perspecitve in this field. Next, several active research fields, such as Evolutionary Optimization on Dynamic/Uncertain Environments, Evolutionary Multi-objective Optimization, Estimation of Distribution Algorithms, Swarm Intelligence, and Game Application, are introduced.

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Many real-world problems from information and communication systems are very complex in nature and quite hard to solve by conventional optimization techniques. Since 1960s, there has been being an increasing interest in imitating living beings to solve such kinds of hard optimization problems. Simulating natural evolutionary process of human beings results in stochastic optimization techniques called evolutionary algorithms (EAs) that can often outperform conventional optimization methods when applied to difficult real-world problems.
In this survey paper, we provide a comprehensive survey of the current state-of-the-art in the applications of EA in information and communication systems. In order to demonstrate the EAs which are powerful and broadly applicable stochastic search and optimization techniques, we deal with the following application areas: communication network, system reliability, image processing, pattern recognition, circuit system, security, and intelligent transportation system.

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Many combinatorial optimization problems from industrial engineering and operations research in real-world are very complex in nature and quite hard to solve them by conventional techniques. Since the 1960s, there has been an increasing interest in imitating living beings to solve such kinds of hard combinatorial optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization techniques called evolutionary algorithms (EAs), which can often outperform conventional optimization methods when applied to difficult real-world problems.
In this survey paper, we provide a comprehensive survey of the current state-of-the-art in the use of EA in manufacturing and logistics systems. In order to demonstrate the EAs which are powerful and broadly applicable stochastic search and optimization techniques, we deal with the following engineering design problems: transportation planning models, layout design models and two-stage logistics models in logistics systems; job-shop scheduling, resource constrained project scheduling in manufacturing system.

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Genetic algorithms (GAs) are search algorithms based on the mechanics of natural selection and natural evolution. Due to cost reasons, hardware accelerators for GAs are required to reduce its execution time. In the hardware implementation of GAs, a circuit design of roulette wheel selection influences the performance of the GAs hardware. In this paper, we propose a new selection circuit based on Rough Comparison Method (RCM), and evaluate effects of the proposed circuit in terms of execution time and circuit size. The RCM is a parallel processing circuit. Therefore, the execution time is constant regardless of the increase of number in individuals, and the circuit size of the RCM is reduced than the ordinary parallel roulette selection circuits.

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In this paper, we propose the melody generation system with the analysis result of an existing melody. In addition, we introduce the device that takes user's favor in the system. The melody generation is done by pitch's being arranged best on the given rhythm. The best standard is decided by using the feature element extracted from existing music by proposed method. Moreover, user's favor is reflected in the best standard by operating some of the feature element in users. And, GA optimizes the pitch array based on the standard, and achieves the system.

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In recent years a lot of Automatic Programming techniques have developed. A typical example of Automatic Programming is Genetic Programming (GP), and various extensions and representations for GP have been proposed so far. However, it seems that more improvements are necessary to obtain complex programs automatically. In this paper we propose a new method called GRAph structured Program Evolution (GRAPE). The representation of GRAPE is graph structure, therefore it can represent complex programs (e.g. branches and loops) using its graph structure. Each program is constructed as an arbitrary directed graph of nodes and data set. The GRAPE program handles multiple data types using the data set for each type, and the genotype of GRAPE is the form of a linear string of integers. We apply GRAPE to five test problems, factorial, Fibonacci sequence, exponentiation, reversing a list and sorting a list, and demonstrate that the optimum solution in each problem is obtained by the GRAPE system.

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This paper proposes a new diploid operation technique with probability for function optimization under non-stationary environments and describes a feature of diploid genetic algorithms, diploid GAs. The advantage of the technique over previous diploid GAs is that one genotype is transformed into many phenotypes with probability. This transformation is not made at random. It has a certain range of probabilistic. Each individual has each range. The range enables to adapt to various environments. The technique allows genes probabilistic representation of dominance, and can keep a diversity of individuals. The experiment results show that the technique can adapt to severe environmental changes where previous diploid GAs cannot adapt. It is shown that the technique is able to find optimum solutions with high probability and that a distribution of individuals changes when the environment changes. Moreover, by comparing proposed diploid GA with haploid GA whose chromosome is twice the length, a feature of diploid is described.

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Evolutionary Multi-objective Optimization (EMO) is expected to be a powerful optimization framework for real world problems such as engineering design. Recent progress in automatic control and instrumentation provides us with a smart environment called Hardware In the Loop Simulation (HILS) for experiment-based optimization. However, since conventional technique of Multi-Objective Evolutionary Algorithms (MOEAs) requires a large number of evaluations, it is difficult to apply it to real world problems of costly evaluation. To make Experiment-Based EMO (EBEMO) using the HILS environment feasible, the most important pre-requisite is reduction of the number of necessary fitness evaluations. In the EBEMO, the performance of the evaluation reduction under uncertainty such as observation noise is highly important, although the previous works often assume noise-free environments. In this paper, we propose an evaluation reduction to overcome the above-mentioned problem by selecting the solution candidates by means of the estimated fitness before applying them to the real experiment in MOEAs. This technique is called ‘Pre-selection’. For the estimation of fitness, we adopt locally weighted regression. The effectiveness of the proposed method was examined by some numerical experiments and also two-objective four-variable optimization problem of a real internal-combustion engine using HILS.

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Recently, a system using the bug-type artificial life was proposed for discovering the function, and has been improved further. This system is one of the extended models of the GA and GP. However, when the observation data is very complicated, the function is occasionally not obtained. In this study, a new concept has been introduced in order that the function search can be applied to the complicated observation data. The function search by the S-System is executed twice or more number of times. This is termed search-accumulation. To confirm the validity of search-accumulation, the Himmelblau function, valley function, and equal-loudness level contours (ISO226) are used as the observation data. Since the distributions of the data are complicated, it is difficult to express them as the function of approximation. By the use of the search-accumulation strategy, the function that is in good agreement with the distribution can be successfully obtained. Thus, the validity of this strategy is confirmed. Search-accumulation is also applicable to GP.

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It is well known that index funds are popular passively managed portfolios and have been used very extensively for the hedge trading. Index funds consist of a certain number of stocks of listed companies on a stock market such that the fund's return rates follow a similar path to the changing rates of the market indices. However it is hard to make a perfect index fund consisting of all companies included in the given market index. Thus, the index fund optimization can be viewed as a combinatorial optimization for portfolio managements. In this paper, we propose an optimization method that consists of a genetic algorithm and a heuristic local search algorithm to make strong linear association between the fund's return rates and the changing rates of market index. We apply the method to the Tokyo Stock Exchange and make index funds whose return rates follow a similar path to the changing rates of Tokyo Stock Price Index (TOPIX). The results show that our proposal method makes the index funds with strong linear association to the market index by small computing time.

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Routing problem is one of the important research issues in communication network fields. In this paper, we consider a bicriteria shortest path routing (bSPR) model dedicated to calculating nondominated paths for (1) the minimum total cost and (2) the minimum transmission delay. To solve this bSPR problem, we propose a new multiobjective genetic algorithm (moGA): (1) an efficient chromosome representation using the priority-based encoding method; (2) a new operator of GA parameters auto-tuning, which is adaptively regulation of exploration and exploitation based on the change of the average fitness of parents and offspring which is occurred at each generation; and (3) an interactive adaptive-weight fitness assignment mechanism is implemented that assigns weights to each objective and combines the weighted objectives into a single objective function. Numerical experiments with various scales of network design problems show the effectiveness and the efficiency of our approach by comparing with the recent researches.

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In the real world, there are a lot of scenes from which the product is made by using the robot, which needs different assembly times to perform a given task, because of its capabilities and specialization. For a robotic assembly line balancing (rALB) problem, a set of tasks have to be assigned to stations, and each station needs to select one robot to process the assigned tasks. In this paper, we propose a hybrid genetic algorithm (hGA) for solving this problem. In the hGA, we use new representation method. Advanced genetic operators adapted to the specific chromosome structure and the characteristics of the rALB problem are used. In order to strengthen the search ability, a local search procedure is integrated under the framework the genetic algorithm. Some practical test instances demonstrate the effectiveness and efficiency of the proposed algorithm.

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We discuss a solution method based on evolutionary technology for the optimal component allocation problem in a series-parallel redundant system. A series-parallel system consists of subsystems that are connected in series and each subsystem consists of interchangeable components in parallel. There are some heuristic methods to approximately solve the optimal component allocation problem for series-parallel systems. We have formulated this problem as a multi-objective optimization problem minimizing the system cost and maximizing the system reliability, and proposed an algorithm that obtains the exact solutions (Pareto solutions) of the problems in an efficient way. Because this problem is one of the NP-complete problems, it is difficult to obtain the optimal solution for the large scale problems and methods that obtain the exact solutions are not known. The algorithm utilizes the depth-first search method to eliminate useless searches and employs the branch-and-bound method to obtain the Pareto solutions. According to the results of our numerical experiments, the algorithm searches the Pareto solutions in practical execution time for not so large scale problems. In order to solve larger scale problems, we propose a Multi-objective Genetic Algorithm (MOGA). We evaluate the ability of the MOGA by comparison with the exact solution method by using various scale problems. Through those experiments, we discuss the characteristics of this problem and analyze the effectiveness of our method.

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In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-rkGA) to solve the resource-constrained multiple project scheduling problem (rc-mPSP) which is well known one of NP-hard problems and the objective in this paper is to minimize total complete time in the project. It is difficult for treating the rc-mPSP problems with traditional optimization techniques. The new approach proposed is based on the hybrid genetic algorithm (flc-rkGA) with fuzzy logic controller (FLC) and the random-key encoding. For these rc-mPSP problems, we demonstrate that the proposed flc-rkGA to solve the rc-mPSP problem yields better results than several heuristic genetic algorithms presented in the computation result.

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Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers.
In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3^rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.

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The logistics network design is one of the important phase of Supply Chain Management (SCM) and it is the problem that should be optimized for long-term promotion of efficiency of the whole supply chain. Usually a plant produces different type of products. Even if it is a factory of the same company, delivery is different by a kind of a produced product. The restrictions which this model has are deeply concerned with TP in the real world. In this paper, we consider the logistics network design problems with multi-products and constraints for delivery course. To solve the problem, we used a hybrid priority-based Genetic Algorithm (h-priGA), and we tried the comparison experiment with priority-based Genetic Algorithm (priGA)and h-priGA, we show it about the effectiveness of h-priGA.

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An optimum design technique of balanced surface acoustic wave (SAW) filters using a differential evolution (DE) is proposed. First of all, in order to evaluate the performance of balanced SAW filters based on the computer simulation, a network model of them is composed by using mixed-mode S-parameters. Then the structural design of balanced SAW filters is formulated as a function optimization problem for improving their performance in both the balance characteristics and the filter characteristics. For solving the function optimization problem, a basic DE is employed. In order to apply the basic DE to the optimization problem effectively, a distorted problem space defined by various design parameters is embedded in a regularized continuous search space. Finally, through the computational experiments conducted on a balanced SAW filter, the usefulness of the proposed optimum design technique is demonstrated. Besides, it is clarified that the non-symmetric structure of SAW filter has a possibility to improve its balance characteristics.

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Recent developments in the areas of mobile and humanoid robots have resulted in a marked increase in the potential applications of robotics. In addition, since it is likely that robots will cohabitate and interact with humans in a variety of situations, they will need an external skin covering capable of distinguishing between various stimuli. This study sought to develop skin sensors for robots that were capable of recognizing four stimuli that are frequently encountered in everyday life. We developed a prototype skin sensor system using two types of shock sensors and pressure-sensitive rubber. The results demonstrated that four stimuli could be distinguished based on differences in pressure and acceleration between each stimulus. We describe the development of the skin sensory system, the mechanism employed to distinguish between the different stimuli, and present the results of experiments conducted using the developed system.

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This paper proposes a method to measure the two-dimensional pose of an object by using a voting process based on geometrical parameters of selected future pixels. The proposed method can measure a location and an inclination of an object even if the object has a free two-dimensional pose in an image. In the template registration process, the geometrical parameters of plural selected future pixels are registered into the template table. In the matching process, a target image area with a similar figure to a template image is searched by using the geometrical parameters of the future pixels. The experimental results show that the poses of objects with free locations and inclinations were recognized successfully by the proposed method with high speed matching in comparison with the conventional methods.

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Bit-length of digital images is decided by the Analog to Digital (AD) converter. The bit-length expansion technique is necessary for the high-quality display such as the liquid crystal display (LCD) and the plasma display. Moreover, bit-length expansion is also necessary for low-bit length images, which are degraded by the pseudo-contour. In this paper, we propose a bit-length expansion method from M-level to N-level (N = 3(M -1)+1). This operation means that two quantization-levels are introduced between existing levels. The bit-length expansion method results in the estimation problem of an error image between an M-level's image and an N-level's image. The error image is the pattern image which is consisted of three values. We show the estimation method of the three values pattern image and the proposed method can remove the pseudo-contour of digital images through a lot of application results.

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In this paper, a nonlinear positioning control scheme is proposed for a class of Euler-Lagrange systems acted by static friction. A compromise method of the constrained control input and the high accuracy position servo requirement is introduced to construct a PD -like controller. Based on the Filippov-framework, it's proved that the equilibrium point set of the closed-loop system is equal to the static solution set, which gives the maximum range of the state-steady servo error. By using the generalized Lyapunov method and Laselle's invariant principle, we prove that any trajectory of the closed-loop system will converge to the stationary set. Moveover, unlike the high gain feedback, the PD -like controller can regulate its feedback gain with respect to the servo error such that it is also available for the input constrained case. Finally, to demonstrate the proposed controller, some simulation results are presented.

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Overload management is of vital importance in wide-area publish/subscribe systems, yet current solutions are best-effort. In this paper, we present an admission control scheme for overload management in large-scale and scalable content-based publish/subscribe systems. We analyze the stumbling block for implementing admission control in publish/subscribe systems, and point out how it differs from admission control schemes in other research areas. We propose a cover relation based algorithm to compute subscription resource requirements, and an admission control algorithm based on subscription routing. The scheme ensures time, space and flows decoupling without sacrificing scalability of publish/subscribe systems. Finally, we conduct experiments to verify the effectiveness of the scheme.

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When RFID tag is used in the whole item lifecycle including a consumer scene or a recycle scene, we have to protect consumer privacy in the state that RFID tag is stuck on an item. We use the low cost RFID tag that has the access control function using a password, and we propose a method which manages RFID tags by passwords identical to each group of RFID tags. This proposal improves safety of RFID system because the proposal method is able to reduce the traceability for a RFID tag, and hold down the influence for disclosure of RFID passwords in the both scenes.

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The resonance characteristics of short-ended half-wavelength coplanar waveguide(CPW) LC tap-coupling resonators are examined theoretically and experimentally. The characteristics near the passband have been improved by applying the presented CPW resonator to a design of a bandpass filter(BPF). Moreover, the difference between the calculation and the measurement results is examined by using the equivalent circuit about the presented BPF.

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This paper proposes a new Tabu Search with multi search points. In the proposed method, Proximate Optimality Principle (POP) is taken into consideration in order to improve its search efficiency. The proposed method is applied to some traveling salesman problems to verify the performance of the proposed algorithm.

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This paper proposes a Particle Swarm Optimization method for constrained optimization problems. The proposed method uses feedback of the swarm activity for maintaining the diversification of its search. The proposed method is applied to 4 typical constrained optimization problems to verify its performance.

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