• Journal of Multimedia Information System
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• v.7 no.2
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• pp.165-174
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• 2020

This document outlines a new color image encryption technology development. After splitting the original image into 240-bit blocks and modifying the first block by an initialization vector, an improved Feistel circuit is applied, sponsored by a genetic crossover operator and then strong chaining between the encrypted block and the next clear block is attached to set up the confusion-diffusion and heighten the avalanche effect, which protects the system from any known attack. Simulations carried out on a large database of color images of different sizes and formats prove the robustness of such a system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.275-284
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• 1999

The TTP(TimeTabling Problem) for a university which should be made every term is very difficult problem to schedule because each component must satisfy all fixed constraints. This TTP, therefore, is solved by several heuristic methods. In this paper, applying PPEP(Parallel Population Evolution Programs) for the TTP, we try to find the approximated optimal solution by maintaining independence of each subpopulation and by searching the wide feasible solution. In addition, we present the problem to be appeared when used PMX(Partially mapped crossover)operator for the long chromosomes and then propose GBX (Gene_Based crossover)operator which can complement it. Finally, experimental results are presented comparing EP(Evolution Programs) and PPEP, then GBX which has the random genes and the selected genes on a real TTP.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.189-203
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• 2002

In this paper we compare the performances of adaptive operators in genetic algorithm. For the adaptive operators, the crossover and mutation operators of genetic algorithm are considered. One fuzzy logic controller is developed in this paper and two heuristics is presented from conventional works for constructing the operators. The fuzzy logic controller and two conventional heuristics adaptively regulate the rates of the operators during genetic search process. All the algorithms are tested and analyzed in numerical examples. Finally, the best algorithm is recommended.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.61-63
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• 2003

지난 10년 동안 유전 알고리즘은 어렵고 복잡한 다양한 문제들을 해결하기 위한 새로운 방법으로 인식되어왔다. 이러한 유전 알고리즘의 성능은 알고리즘 내에 구현되는 여러 연산자들에 좌우된다. 따라서 많은 연구자들이 새로운 연산자 개발에 관심을 가져 왔었다. 특히, 가장 널리 알려진 조합최적화 문제 중에 하나인 알려진 traveling salesman problem (TSP)의 경우 NP-hard문제로 분류되어 현재까지 이를 해결하기 위한 다양한 유전 연산자들이 개발되어 왔었다. 따라서 본 논문에서는 TSP 문제를 test problem로 이용하여 이를 해결하기 위한 새로운 유전 연산자 특히 교차 (Crossover Operator) 연산자들을 제안하고 기존의 다양한 연산자들과 비교를 통해서 성능을 입증한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1521-1524
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• 2003

In this paper, we propose a "Distance Correlation Selection operator (DCS)" as a new selection operator. For Genetic Algorithm (GA), many improvements have been proposed. The MGG (Minimal Generation Gap) model proposed by Satoh et.al. shows good performance. The MGG model has all advantages of conventional models and the ability of avoiding the premature convergence and suppressing the evolutionary stagnation. The proposed method is an extension of selection operator in the original MGG model. Generally, GA has two types of selection operators, one is "selection for reproduction", and the other is "selection for survival"; the former is for crossover and the latter is the individuals which survive to the next generation. The proposed method is an extension of the former. The proposed method utilizes distance information between individuals. From this extension, the proposed method aims to expand a search area and improve ability to search solution. The performance of the proposed method is examined with several standard test functions. The experimental results show good performance better than the original MGG model.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.4
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• pp.67-72
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• 2012

In this paper a new method of handling linear constraints for the genetic algorithm is suggested. The method is designed to maintain the feasibility of offsprings during the evolution process of the genetic algorithm. In the genetic algorithm, the chromosomes are coded as the vectors in the real vector space constrained by the linear constraints. A method of handling the linear constraints already exists in which all the constraints of equalities are eliminated so that only the constraints of inequalities are considered in the process of the genetic algorithm. In this paper a new method is presented in which all the constraints of inequalities are eliminated so that only the constraints of equalities are considered. Several genetic operators such as arithmetic crossover, simplex crossover, simple crossover and random vector mutation are designed so that the resulting offspring vectors maintain the feasibility subject to the linear constraints in the framework of the new handling method.

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.14-21
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• 2009

We formulate a mathematical model of remanufacturing system as multi-stage reverse Logistics Network Problem (mrLNP) with minimizing the total costs for reverse logistics shipping cost and inventory holding cost at disassembly centers and processing centers over finite planning horizons. For solving this problem, in the 1st and the 2nd stages, we propose a Genetic Algorithm (GA) with priority-based encoding method combined with a new crossover operator called as Weight Mapping Crossover (WMX). A heuristic approach is applied in the 3rd stage where parts are transported from some processing centers to one manufacturer. Computer simulations show the effectiveness and efficiency of our approach. In numerical experiments, the results of the proposed method are better than pnGA (Prufer number-based GA).

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1210-1218
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• 2011

GA (Genetic Algorithms) are efficient for searching for global optima but may have some problems such as premature convergence, convergence to local extremum and divergence. These phenomena are related to the evolutionary operators. As population diversity converges to low value, the search ability of a GA decreases and premature convergence or converging to local extremum may occur but population diversity converges to high value, then genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we analyze the effects of the selection operator, crossover operator, and mutation operator on convergence properties, and propose the sweeping method of mutation probability and elitist propagation rate to maintain the diversity of the GA's population for getting out of the premature convergence. Results of simulation studies verify the feasibility of using these sweeping operators to avoid premature convergence and convergence to local extrema.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.1-22
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• 2011

In this paper, we propose an adaptive genetic algorithm (aGA) approach for effectively solving the sequencing problem with precedence constraints (SPPC). For effective representation of the SPPC in the aGA approach, a new representation procedure, called the topological sort-based representation procedure, is used. The proposed aGA approach has an adaptive scheme using a fuzzy logic controller and adaptively regulates the rate of the crossover operator during the genetic search process. Experimental results using various types of the SPPC show that the proposed aGA approach outperforms conventional competing approaches. Finally the proposed aGA approach can be a good alternative for locating optimal solutions or sequences for various types of the SPPC.