• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.265-276
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• 2004

Genetic Algorithm(GA), that is shown stable performance to find an optimal solution, has been used as a method of solving large-scaled optimization problems with complex constraints in various applications. Since it takes so much time to execute a long computation process for iterative evolution and adaptation. In this paper, a hardware-based adaptive GA was proposed to reduce the serious computation time of the evolutionary process and to improve the accuracy of convergence to optimal solution. The proposed GA, based on steady-state model among continuos generation model, performs an adaptive mutation process with consideration of the evolution flow and the population diversity. The drawback of the GA, premature convergence, was solved by the proposed adaptation. The Performance improvement of convergence accuracy for some kinds of problem and condition reached to 5-100% with equivalent convergence speed to high-speed algorithm. The proposed adaptive GAP(Genetic Algorithm Processor) was implemented on FPGA device Xilinx XCV2000E of EHW board for face recognition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.468-475
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• 2003

In this Paper, we design an optimal binary phase computer generated hologram for Pattern generation using combined genetic algorithm and simulated annealing algorithm together. To design an optimal binary phase computer generated hologram, in searching process of the proposed method, the simple genetic algorithm is used to get an initial random transmittance function of simulated annealing algorithm. Computer simulation shows that the proposed algorithm has better performance than the genetic algorithm or simulated annealing algorithm of terms of diffraction efficiency

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.291-295
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• 1997

현재에 주로 개발되어 상용화가 시작되고 있는 음성인식 시스템의 대부분은 단어인식을 기분으로 하는 시스템으로 적용 단어수를 늘려줌으로서 인식범위를 늘일 수 있으나, 그에 따라 검색해야하는 단어수가 늘어남으로서 전체적인 시스템의 속도 및 성능이 저하되는 경향이 있다. 이러한 단점의 극복을 위하여 본 논문에서는 HMM(Hidden Markov Model)과 GA(Genetic Algorithm)를 이용한 한국어 음성의 음소단위 인식 시스템을 구현하였다. 음성 특징으로는 LPC Cepstrum 계수를 사용하였으며, 인식시는 인식대상이 되는 단어에 대하여 GA(Genetic Algorithm)을 통하여 각 음소를 분리하고, 음소단위로 학습된 HMM 파라미터를 적용하여 인식함으로써 각각의 음소별 가능하도록 하는 방법을 제안하였다.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.3
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• pp.289-295
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• 2000

The triangulation problem is to make triangles using the given points on the space. The Minimum Weight Triangulation(MWT) is the problem of finding a set of triangles with the minimum weight among possible set of the triangles. In this paper, a modified genetic algorithm(GA) based on an existing genetic algorithm and multispace smoothing technique is proposed. Through the computational results, we can find the tendency that the proposed GA finds good solutions though it needs longer time than the existing GA does as the problem size increases.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.545-552
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• 1999

The Maintenance scheduling is one of the mid-term scheduling problems systems. There have been many methods for this problem, but there is no effective way to treat all the generators simultaneously. In this paper, we apply a genetic algorithm(GA) to the maintenance scheduling problem. We proposed new crossover operators(BOX type crossover) to improve searching ability of GA. Satisfactory results are obtained by GA with the proposed corssover operators.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.114-116
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• 2015

This paper presents a Stochastic Time-Cost Tradeoff analysis system (STCT) that identifies optimal construction methods for activities, hence reducing the project completion time and cost simultaneously. It makes use of schedule information obtained from critical path method (CPM), applies alternative construction methods data obtained from estimators to respective activities, computes an optimal set of genetic algorithm (GA) parameters, executes simulation based GA experiments, and identifies near optimal solution(s). A test case verifies the usability of STCT.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.63-82
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• 2007

In this paper, we propose an adaptive hybrid genetic algorithm (ahGA) approach for effectively solving multistage-based scheduling problems in flexible manufacturing system (FMS) environment. The proposed ahGA uses a neighborhood search technique for local search and an adaptive scheme for regulation of GA parameters in order to improve the solution of FMS scheduling problem and to enhance the performance of genetic search process, respectively. In numerical experiment, we present two types of multistage-based scheduling problems to compare the performances of the proposed ahGA with conventional competing algorithms. Experimental results show that the proposed ahGA outperforms the conventional algorithms.

• 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.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.463-467
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• 2008

Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1938-1948
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• 2003

This paper represents that an enhanced genetic algorithm (EGA) is applied to optimal design of a squeeze film damper (SFD) to minimize the maximum transmitted load between the bearing and foundation in the operational speed range. A general genetic algorithm (GA) is well known as a useful global optimization technique for complex and nonlinear optimization problems. The EGA consists of the GA to optimize multi-modal functions and the simplex method to search intensively the candidate solutions by the GA for optimal solutions. The performance of the EGA with a benchmark function is compared to them by the IGA (Immune-Genetic Algorithm) and SQP (Sequential Quadratic Programming). The radius, length and radial clearance of the SFD are defined as the design parameters. The objective function is the minimization of a maximum transmitted load of a flexible rotor system with the nonlinear SFDs in the operating speed range. The effectiveness of the EGA for the optimal design of the SFD is discussed from a numerical example.