• 산업경영시스템학회지
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• 제39권4호
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• pp.81-89
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• 2016

Distributed genetic algorithm (DGA), also known as island model or coarse-grained model, is a kind of parallel genetic algorithm, in which a population is partitioned into several sub-populations and each of them evolves with its own genetic operators to maintain diversity of individuals. It is known that DGA is superior to conventional genetic algorithm with a single population in terms of solution quality and computation time. Several researches have been conducted to evaluate effects of parameters on GAs, but there is no research work yet that deals with structure of DGA. In this study, we tried to evaluate performance of various genetic algorithms (GAs) for the famous symmetric traveling salesman problems. The considered GAs include a conventional serial GA (SGA) with IGX (Improved Greedy Crossover) and several DGAs with various combinations of crossover operators such as OX (Order Crossover), DPX (Distance Preserving Crossover), GX (Greedy Crossover), and IGX. Two distinct immigration policies, conventional noncompetitive policy and newly proposed competitive policy are also considered. To compare performance of GAs clearly, a series of analysis of variance (ANOVA) is conducted for several scenarios. The experimental results and ANOVAs show that DGAs outperform SGA in terms of computation time, while the solution quality is statistically the same. The most effective crossover operators are revealed as IGX and DPX, especially IGX is outstanding to improve solution quality regardless of type of GAs. In the perspective of immigration policy, the proposed competitive policy is slightly superior to the conventional policy when the problem size is large.

• 전자공학회논문지SC
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• 제38권5호
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• pp.7-13
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• 2001

단일 신경망에 기반한 웨이블릿 이론과 모듈라 개념을 결합하여 기존의 웨이블릿 신경망이나 모듈라 네트워크의 일종인 모듈라 웨이블릿 신경망이 제안되었다. 본 논문에서는 유전 알고리즘을 사용하여 모듈라 웨이블릿 신경망의 최적구조를 효과적으로 설계하는 방법을 제시하였다. 각 모듈을 구성하는 웨이블릿 신경망의 웨이블릿 기저함수의 팽창과 이동계수를 결장하기 위해 유전 알고리즘을 사용하였다. 제안한 최적 구조 설계 알고리즘을 근사화 문제에 적용하여 우수성을 검증하였다.

• 한국정보과학회논문지:시스템및이론
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• 제33권6호
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• pp.316-325
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• 2006

인간과 같은 2배체의 각 염색체는 부모로부터 물려받은 2벌의 복제로 이루어져 있다. 이들 각 복제에서 SNP(single nucleotide polymorphism) 서열 정보를 하플로타입이라 부른다. 인간의 하플로타입 지도를 완전히 찾는 것은 인간 지놈의 중요한 작업 중의 하나인데, 실험적인 방법으로 하플로타입을 직접 얻는 것은 시간이 많이 걸리고 비용이 많이 든다. 따라서 두 하플로타입 정보가 혼합된 지노타입의 샘플들로부터 하플로타입을 추론하는 것에 대하여 연구되어왔다. 이 논문에서는 지노타입들을 설명하는 최소 개수의 하플로타입들을 찾는 모델(최소 하플로타입 추론문제)에 근거하여, 유전자 알고리즘을 사용하여 하플로타입을 추론하는 새로운 접근 방법을 제시한다. 좋은 결과를 주는 것으로 알려진 HAPAR[1]와 이 논문에 제시한 알고리즘을 컴퓨터 실험에 의한 비교를 통하여, 입력이 클 때 이 논문의 알고리즘이 수행시간은 적게 걸리면서 정확성이 비슷함을 보인다. 또한 이 실험 결과를 최근에 제시된 방법인 PTG[2]와 비교한다.

• 산업경영시스템학회지
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• 제38권3호
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• pp.169-180
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• 2015

Recently, scheduling problems with position-dependent processing times have received considerable attention in the literature, where the processing times of jobs are dependent on the processing sequences. However, they did not consider cases in which each processed job has different learning or aging ratios. This means that the actual processing time for a job can be determined not only by the processing sequence, but also by the learning/aging ratio, which can reflect the degree of processing difficulties in subsequent jobs. Motivated by these remarks, in this paper, we consider a two-agent single-machine scheduling problem with linear job-dependent position-based learning effects, where two agents compete to use a common single machine and each job has a different learning ratio. Specifically, we take into account two different objective functions for two agents: one agent minimizes the total weighted completion time, and the other restricts the makespan to less than an upper bound. After formally defining the problem by developing a mixed integer non-linear programming formulation, we devise a branch-and-bound (B&B) algorithm to give optimal solutions by developing four dominance properties based on a pairwise interchange comparison and four properties regarding the feasibility of a considered sequence. We suggest a lower bound to speed up the search procedure in the B&B algorithm by fathoming any non-prominent nodes. As this problem is at least NP-hard, we suggest efficient genetic algorithms using different methods to generate the initial population and two crossover operations. Computational results show that the proposed algorithms are efficient to obtain near-optimal solutions.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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• pp.89-92
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• 2000

In this paper, an effective image restoration using Genetic Algorithm(GA) in wavelet transform region is proposed. First, a wavelet transform is used for decomposition of a blurred image with white Gaussian noise as a preprocessing of the proposed method. The wavelet transform decomposes a degraded image into a wavelet subband coefficient planes. In this wavelet transformed subband coefficient planes, three highest subbands is composed entirely of noise elements on a degraded image. So, these subbands are removed. And remained subbands except for the lowest subband are individually applied to GA. For the performance evaluation, the proposed method is compared with a conventional single GA algorithm and a conventional hybrid method of wavelet transform and GA for a Lenna image and a boat image. As an experimental result, the proposed algorithm is prior to a conventional methods as each PSNR 3.4dB, 1.3dB.

• 전자공학회논문지B
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• 제32B권7호
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• pp.1025-1035
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• 1995

In this paper, we realize a real time digital FM synthesizer based on genetic algorithm using a general purpose digital signal processor. Especially, we synthesize diverse music sounds nicely using a synthesis model consisting of a single modulator and multiple carriers. Also we present genetic algorithm-based technique which determines optimal parameters for reconstruction through FM synthesis of a sound after analyzing the spectrum of PCM data as a standard music sound using FFT. Using the suggested parameter extractiuon algorithm, we extract parameters of several instruments and then synthesize digital FM sounds. To verify the validity of the parameter extraction algorithm as well as realization of a real time digital music synthesizer, the evaluation is first done by listening the sound directly as subjective test. Secondly, to evaluate the synthesized sound objectively with an engineering sense, we compare the synthesized sound with an original one in a time domain and a frequency domain.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.217-229
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• 2007

In this paper, Genetic Algorithm (GA) is used to find the Maximum Weight Independent Set (MWIS) of a graph. First, MWIS problem is formulated as a 0-1 integer programming optimization problem with linear objective function and a single quadratic constraint. Then GA is implemented with the help of this formulation. Since GA is a heuristic search method, exact solution is not reached in every run. Though the suboptimal solution obtained is very near to the exact one. Computational result comprising an average performance is also presented here.

• Structural Engineering and Mechanics
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• 제47권5호
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• pp.729-740
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• 2013

This paper addresses the structural design of the front end of Siemens ER24 locomotive body. The steel structure of the frontend is replaced with composite. Optimization of the composite lay-up is performed using Genetic Algorithms. Initially an optimized single design for the entire structure is presented. Then a more refined optimum is developed by considering the separate optimization of 7 separate regions of the structure. Significant savings in the weight of the structure are achieved.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1995년도 봄 학술발표회 논문집
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• pp.187-194
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• 1995

Traditional genetic algorithms(GA) have mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its targe computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of read code GA are developed to use continuous design variables directly. The results of real code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As results of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the rent code GA developed here can be used for the general optimization problem.

• Journal of Advanced Marine Engineering and Technology
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• 제24권2호
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• pp.63-71
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• 2000

This paper deals with a systematic approach to GA-PI controller design for static VAR compensator(SVC) using genetic algorithm(GA) to improve system stability. Genetic algorithms(GAs) are search algorithms based on the mechanics of natural selection and natural genetics. To verify the validity of the proposed method, investigated damping ratio of the eigenvalues of the electro-mechanical modes system with and without SVC. Also, we considered dynamic response of terminal speed deviation and terminal voltage deviation by applying a power fluctuation at heavy load, normal load and light to verify the robustness of the proposed. Thus, we proved usefulness of GA-PI controller design to improve the stability of single machine-bus with SVC system.