• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.48-51
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• 2000

The traveling salesman problem with precedence relations (TSPPR) is harder than general traveling salesman problem. In this paper we propose an efficient genetic algorithm (GA) to solve the TSPPR. The key concept of the proposed genetic algorithm is a topological sort (TS). The results of numerical experiments show that the proposed GA approach produces an optimal solution for the TSPPR.

• Journal of Multimedia Information System
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• v.4 no.2
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• pp.89-92
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• 2017

This paper investigates simple gray level image enhancement technique based on Genetic Algorithms and Local Statistics. The task of GA is to adapt the parameters of local sliding masks over pixels, finding out the best parameters preserving the brightness and possibly preventing the creation of intensity artifacts in the local area of images. The algorithm is controlled by GA as to enhance the contrast and details in the images automatically according to an object fitness criterion. Results obtained in terms of subjective and objective evaluations, show the plausibility of the method suggested here.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.939-944
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• 2001

Genetic algorithm is modelled on natural evolution and simulated annealing is based on the simulation of thermal annealing. Both genetic algorithm and simulated annealing are stochastic method. So they can find global optimum values. For compare efficiency of SA and GA's, some function value was maximized. In the result, that was a little better than GA's.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.119-126
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• 2006

The task scheduling in multiprocessor system is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost practical cases, an NP-hard problem. Consequently algorithms based on various modern heuristics have been proposed for practical reason. This paper proposes a new task scheduling algorithm using Genetic Algorithm which combines simulated annealing (GA+SA) in multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the result of proposed algorithm is better than that of any other algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.75-87
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• 2009

We propose in this paper a novel approach to image fusion in which the fusion rule is guided by optimizing an image clarity function. A Genetic Algorithm is used to stochastically select, comparative to the clarity function, the optimum block from among the source images. A novel nested Genetic Algorithm with gifted individuals found through bombardment of genes by the mutation operator is designed and implemented. Convergence of the algorithm is analytically and empirically examined and statistically compared (MANOVA) with the canonical GA using 3 test functions commonly used in the GA literature. The resulting GA is invariant to parameters and population size, and a minimal size of 20 individuals is found to be sufficient in the tests. In the fusion application, each individual in the population is a finite sequence of discrete values that represent input blocks. Performance of the proposed technique applied to image fusion experiments, is characterized in terms of Mutual Information (MI) as the output quality measure. The method is tested with C=2 input images. The results of the proposed scheme indicate a practical and attractive alternative to current multi-focus image fusion techniques.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2226-2229
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• 2003

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. In GA optimization, neuron spreads were represented in a chromosome. The proposed optimization method was applied to a data set, consisted of 4 different promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The range of neuron spreads was experimentally varied from 0.4 to 1.4 with an increment of 0.1. The GA-GRNN was compared to a conventional GRNN. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. The GA-GRNN significantly improved the total classification sensitivity compared to the conventional GRNN. Also, the GA-GRNN demonstrated an improvement of about 10.1% in the total prediction accuracy. As a result, the proposed GA-GRNN illustrated improved classification sensitivity and prediction accuracy over the conventional GRNN.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.500-504
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• 2001

The present approach based on the genetic algorithm with improved selections of bonds was adopted to extract a bridged T equivalent circuit elements of $\times10\mu m^2$InGaP/GaAs HBT. the small-signal model parameters were extracted using the genetic algorithm from S-parameters measured at different frequencies under multiple forward-active biases, which demonstrate physically meaningful values and consistency. The agreement between the measured and modeled S-parameters is excellent over the frequency range of 2 to 26.5GHz.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.4
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• pp.113-128
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• 2016

This paper proposes a Bi-level Genetic Algorithm for the Fixed Charge Transportation Problem with Non-linear Unit Cost. The problem has the property of mixed integer program with non-linear objective function and linear constraints. The bi-level procedure consists of the upper-GA and the lower-GA. While the upper-GA optimize the connectivity between each supply and demand pair, the lower-GA optimize the amount of transportation between the pairs set to be connected by the upper-GA. In the upper-GA, the feasibility of the connectivity are verified, and if a connectivity is not feasible, it is modified so as to be feasible. In the lower-GA, a simple method is used to obtain a pivot feasible solution under the restriction of the connectivity determined by the upper-GA. The obtained pivot feasible solution is utilized to generate the initial generation of chromosomes. The computational experiment is performed on the selected problems with several non-linear objective functions. The performance of the proposed procedure is analyzed with the result of experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.462-466
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• 2002

Genetic Algorithm (GA) which imitates the process of nature evolution is applied to various fields because it is simple to theory and easy to application. Recently applying GA to hardware, it is to proceed the research of Evolvable Hardware(EHW) developing the structure of hardware and reconstructing it. And it is growing a necessity of GAP that embodies the computation of GA to the hardware. Evolving by GA don't act in the software but in the hardware(GAP) will be necessary for the design of independent EHW. This paper shows the design GAP for fast reconfiguration of EHW.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.107.6-107
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• 2001

Genetic alogorithm(GA) is a well-known global optimization algorithm. However, as the searching bounds grow wider., performance of local optimization deteriorates. In this paper, we propose a hybrid algorithm which integrates the gradient algorithm and GA so as to reinforce the performance of local optimization. We apply this algorithm to the system identification of second order RLC circuit. Identification results show that the proposed algorithm gets the better and robust performance to find the exact values of RLC elements.