This paper presents an efficient graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG) and its extension to a parallel version. A new version of parallel EGG system is based on a coarse-grained model of parallel processing and can synthesize heterogeneous networks of various different components efficiently. The potential capability of parallel EGG system is demonstrated through the design of current-mode logic circuits.

A new blind restoration method applying Real-coded genetic algorithm (RcGA) will be proposed, and this method will be proven valid for the blurred image restoration with unidentified degradation in the experiments. In this restoration method, the degraded and blurred image is going to get restricted to the images possible to be expressed in the point spread function (PSF), then the restoration filter for this degraded image, which is also the 2-dimentional inverse filter, will be searched among several points applying RcGA. The method will enable to seek efficiently among vast solution space consists of numeral coefficient filters. And perceiving the essential features of the spectrum in the frequency space, an evaluation function will be proposed. Also, it will be proposed to apply the Rolling-ball transform succeeding an appropriate Gaussian degrade function against the dual degraded image with blur convoluting impulse noise. By above stated features of this restoration method, it will enable to restore the degraded image closer to the original within a practical processing time. Computer simulations verify this method for image restoration problem when the factors causing image distortions are not identified.

In this work we study the performance of a distributed GA that incorporates in its core parallel cooperative-competitive genetic operators. A series of controlled experiments are conducted using various large and difficult 0/1 multiple knapsack problems to test the robustness of the distributed GA. Simulation results verify that the proposed distributed GA compared with a canonical distributed GA significantly gains in search speed and convergence reliability with less communication cost for migration.

Feature subset selection is an important preprocessing task for pattern recognition, machine learning or data mining applications. A Genetic Algorithm (GA) with a fuzzy fitness function has been proposed here for finding out the optimal subset of features from a large set of features. Genetic algorithms are robust but time consuming, specially GA with neural classifiers takes a long time for reasonable solution. To reduce the time, a fuzzy measure for evaluation of the quality of a feature subset is used here as the fitness function instead of classifier error rate. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function. Simulation over two data sets shows that the proposed algorithm is efficient for selection of near optimal solution in practical problems specially in case of large feature set problems.

An image halftoning technique that uses a simple GA has proven to be effective generating bi-level halftone images with quality higher than conventional techniques. Many devices are designed to handle more than two halftone levels and a GA based multi-level halftoning technique is desirable. In this paper we extend the bi-level halftoning technique to generate multi-level halftone images. Also we introduce an improved GA (GA-SRM) into the proposed multi-level halftoning technique. Experimental results show that the proposed technique can effectively generate high quality multi-level halftone images and that the inclusion of GA-SRM substantially contributes reducing memory usage and accelerating image generation.

Software with design flaws increases maintenance costs, decreases component reuse, and reduces software life. Even well-designed software tends to deteriorate with time as it undergoes maintenance. Work on restructuring object-oriented designs involves estimating the quality of the designs using metrics, and automating transformations that preserve the behavior of the designs. However, these factors have been treated almost independently of each other. A long-term goal is to define transformations preserving the behavior of object-oriented designs, and automate the transformations using metrics. In this paper, we describe a genetic algorithm based restructuring approach using metrics to automatically modify object-oriented designs. Cohesion and coupling metrics based on abstract models are defined to quantify designs and provide criteria for comparing alternative designs. The abstract models include a call-use graph and a class-association graph that represent methods, attributes, classes, and their relationships. The metrics include cohesion, inheritance coupling, and interaction coupling based on the behavioral similarity between methods extracted from the models. We define restructuring operations, and show that the operations preserve the behavior of object-oriented designs. We also devise a fitness function using cohesion and coupling metrics, and automatically restructure object-oriented designs by applying a genetic algorithm using the fitness function.

A study on improving the performance of the real-coded genetic algorithm for electromagnetic inverse scattering of two-dimensional perfectly conducting cylinders is presented. Three schemes, namely, the penalty function approach, the closed cubic B-splines local shape function approach and the adaptive hybrid algorithm approach are proposed to deal with the problem. These schemes can be used separately or be combined to improve the performance. Numerical examples validate the schemes.

In optical networks, wavelength converters are required to improve the efficiency of wavelength-division multiplexing. In this paper, we propose a genetic algorithm to determine the optimal locations of the nodes in the network where a given number of converters are placed. Optimality is achieved by the minimum wavelength blocking probability. Our algorithm is applied to two realistic networks constructed from the locations of major cities in Ibaraki Prefecture and from those in Kanto District in Japan and is shown to reach the nearly optimal solution in a limited number of generations. The accuracy is verified by simulation. The computational time is compared with that of an exhaustive search algorithm.

The redundancy allocation problem for a series-parallel system is a well known as one of NP-hard combinatorial problems and it generally belongs to the class of nonlinear integer programming (nIP) problem. Many researchers have developed the various methods which can be roughly categorized into exact solution methods, approximate methods, and heuristic methods. Though each method has both advantages and disadvantage, the heuristic methods have been received much attention since other methods involve more computation effort and usually require larger computer memory. Genetic algorithm (GA) as one of heuristic optimization techniques is a robust evolutionary optimization search technique with very few restrictions concerning with the various design problems. However, GAs cannot guarantee the optimality and sometimes can suffer from the premature convergence situation of its solution, because it has some unknown parameters and it neither uses a priori knowledge nor exploits the local search information. To improve these problems in GA, this paper proposes an effective hybrid genetic algorithm based on, 1) fuzzy logic controller (FLC) to automatically regulate GA parameters and 2) incorporation of the iterative hill climbing method to perform local exploitation around the near optimum solution for solving redundancy allocation problem. The effectiveness of this proposed method is demonstrated by comparison results with other conventional methods on two different types of redundancy allocation problems.

The group organization used for group learning in a knowledge intensive domain like software development affects educational achievement. This paper proposes a group organization system for software engineering education done through group learning. The organizational problem itself is defined and why a Genetic Algorithm (GA) is an appropriate means of solving this problem is explained. This system is a Web application developed with open source software and runs on an open source software platform. Based on the group organization data collected from actual classes, we generated various group organizations by using different strategy parameter values. We then gave a questionnaire to actual students asking them which solution produced the fairest group organization. The replies received revealed that the candidate solution that set greater weight on leadership capability and system analysis capability was the fairest.

In computer-aided drafting and design, interactive graphics is used to design components, systems, layouts, and structures. There are several approaches for using automated graphical layout tools currently. Our approach employs a genetic algorithm to implement a tool for automated 3D graphical layout design and presentation. The effective use of a genetic algorithm in automated graphical layout design relies on defining a fitness function that reflects user preferences. In this paper, we describe a method to define fitness functions and chromosome structures of selected objects. A learning mechanism is employed to adjust the fitness values of the objects in the selected layout chosen by the user. In our approach, the fitness functions can be changed adaptively reflecting user preferences. Experimental results revealed good performance of the adaptive fitness functions in our proposed mechanism.

In this study, a hybrid genetic algorithm/neural network with fuzzy logic controller (NN-flcGA) is proposed to find the global optimum of reliability assignment/redundant allocation problems which should be simultaneously determined two different types of decision variables. Several researchers have obtained acceptable and satisfactory results using genetic algorithms for optimal reliability assignment/redundant allocation problems during the past decade. For large-size problems, however, genetic algorithms have to enumerate numerous feasible solutions due to the broad continuous search space. Recently, a hybridized GA combined with a neural network technique (NN-hGA) has been proposed to overcome this kind of difficulty. Unfortunately, it requires a high computational cost though NN-hGA leads to a robuster and steadier global optimum irrespective of the various initial conditions of the problems. The efficacy and efficiency of the NN-flcGA is demonstrated by comparing its results with those of other traditional methods in numerical experiments. The essential features of NN-flcGA namely, 1) its combination with a neural network (NN) technique to devise initial values for the GA, 2) its application of the concept of a fuzzy logic controller when tuning strategy GA parameters dynamically, and 3) its incorporation of the revised simplex search method, make it possible not only to improve the quality of solutions but also to reduce computational cost.

A novel rough neural network (RNN) structure and its application are proposed in this paper. We principally introduce its architecture and training algorithms: the genetic training algorithm (GA) and the tabu search training algorithm (TSA). We first compare RNN with the conventional NN trained by the BP algorithm in two-dimensional data classification. Then we compare RNN with NN by the same training algorithm (TSA) in functional approximation. Experiment results show that the proposed RNN is more effective than NN, not only in computation time but also in performance.

In this paper, a hardware-oriented Genetic Algorithm (GA) was proposed in order to save the hardware resources and to reduce the execution time of GAP. Based on steady-state model among continuous generation model, the proposed GA used modified tournament selection, as well as special survival condition, with replaced whenever the offspring's fitness is better than worse-fit parent's. The proposed algorithm shows more than 30% in convergence speed over the conventional algorithm. Finally, by employing the efficient pipeline parallelization and handshaking protocol in proposed GAP, above 30% of the computation speed-up can be achieved over survival-based GA which runs one million crossovers per second (1 MHz), when device speed and size of application are taken into account on prototype. It would be used for high speed processing such of central processor of evolvable hardware, robot control and many optimization problems.

In this paper, genetic algorithms is employed to determine the shape of a conducting cylinder buried in a half-space. Assume that a conducting cylinder of unknown shape is buried in one half-space and scatters the field incident from another half-space where the scattered filed is measured. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization problem. The genetic algorithm is then employed to find out the nearly global extreme solution of the object function such that the shape of the conducting scatterer can be suitably reconstructed. In our study, even when the initial guess is far away from the exact one, the genetic algorithm can avoid the local extremes and converge to a reasonably good solution. In such cases, the gradient-based methods often get stuck in local extremes. Numerical results are presented and good reconstruction is obtained both with and without the additive Gaussian noise.

In order to maintain the diversity of structures in the population and prevent premature convergence, I have developed a new genetic algorithm called DCGA. In the experiments on many standard benchmark problems, DCGA showed good performances, whereas with harder problems, in some cases, the phenomena were observed that the search was stagnated at a local optimum despite that the diversity of the population is maintained. In this paper, I propose methods for escaping such phenomena and improving the performance by reinitializing the population, that is, a method called each-structure-based reinitializing method with a deterministic structure diverging procedure as a method for producing new structures and an adaptive improvement probability bound as a search termination criterion. The results of experiments demonstrate that DCGA becomes robust in harder problems by employing these proposed methods.

An automated analog circuit synthesis based on reuse of topological features of 'prototype circuits' is proposed. The prototype circuits are designed by humans and suggested to the synthesis system as hints of configurations of new analog circuits to be synthesized by the system. The connections of elements in analog circuits are not generally systematic, but they would have some similarities to a circuit which has similar behaviors or functionalities. In the proposed process, the information on circuit connections is stored as sub-circuits extracted from the prototype circuits. And then, genetic algorithm is used to search for an optimum combination of the sub-circuits that achieves the desired electronic specifications. The combinations of sub-circuits are performed with a novel technique where the terminals of the sub-circuits are shared. The capabilities of the proposed method are demonstrated through an example of the synthesis.

This paper proposes the Vector Evaluated GA-ICT (VEGA-ICT), a novel design method that employs the Genetic Algorithm (GA) to obtain the optimum antenna design. GA-ICT incorporates an arbitrary wire-grid model antenna to derive the optimum solution without any basic structure or limitation on the number of elements by merely optimizing an objective function. GA-ICT comprises the GA and an analysis method, the Improved Circuit Theory (ICT), with the following characteristics. (1) To achieve optimization of an arbitrary wire-grid model antenna without a basic antenna structure, the unknowns of the ICT are directly assigned to variables of the GA in the GA-ICT. (2) To achieve a variable number of elements, duplicate elements generated by using the same feasible region are deleted in the ICT. (3) To satisfy all complex design conditions, the GA-ICT generates an objective function using a weighting function generated based on electrical characteristics, antenna configuration, and size. (4) To overcome the difficulty of convergence caused by the nonlinearity of each term in the objective function, GA-ICT adopts a vector evaluation method. In this paper, the novel GA-ICT method is applied to downsize sector antennas. The calculation region in GA-ICT is reduced by adopting cylindrical coordinates and a periodic imaging structure. The GA-ICT achieves a 30% reduction in size compared to the previously reported small sector antenna, MS-MPYA, while retaining almost the same characteristics.

We propose the kernel-based pattern recognition hardware and its design methodology using the genetic algorithm. In the proposed design methodology, pattern data are transformed into the truth tables and the truth tables are evolved to represent kernels in the discrimination functions for pattern recognition. The evolved truth tables are then synthesized to logic circuits. Because of this data direct implementation approach, no floating point numerical circuits are required and the intrinsic parallelism in the pattern data set is embedded into the circuits. Consequently, high speed recognition systems can be realized with acceptable small circuit size. We have applied this methodology to the image recognition and the sonar spectrum recognition tasks, and implemented them onto the newly developed FPGA-based reconfigurable pattern recognition board. The developed system demonstrates higher recognition accuracy and much faster processing speed than the conventional approaches.

This letter presents an efficient graph-based evolutionary optimization technique, and its application to the transistor-level design of multiple-valued arithmetic circuits. The key idea is to introduce "circuit graphs with colored terminals" for modeling heterogeneous networks of various components. The potential of the proposed approach is demonstrated through experimental synthesis of a radix-4 signed-digit (SD) full adder circuit.