• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.335-338
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• 2012

유전 알고리즘 (Genetic Algorithm)은 기존의 알고리즘 개발방법을 통하여 해결하기 어려운 최적화 등의 문제를 해결하기 위한 자연계의 진화과정을 모방한 방법이다. 본 연구에서는 유전 알고리즘을 이용하여 KOSPI 200에서 거래되고 있는 증권의 매수/매도 종목을 추천하는 방법을 제시한다. 이를 위하여 기술적 분석 (Technical Analysis) 방법 중에서 Slow Stochastic 지표와 MACD 지표를 이용하여, 매일매일 두 지표가 나타내는 매매 신호를 기반으로 해당하는 각각의 종목에 대해 최근 가장 좋은 수익률을 나타내는 매수/매도 종목을 추천하는 방법을 구현한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.201-209
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• 2014

This paper proposes a genetic algorithm to maximize the connectivity among the mobile nodes for the cooperative communication in ad-hoc networks. In general, as the movement of the mobile nodes in the networks increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time for a high-density network, we propose a genetic algorithm to obtain the optimal solution for maximizing the connectivity. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the genetic algorithm. We evaluate those performances through some experiments in terms of the maximum number of connections and the execution time of the proposed algorithm. The comparison results show that the proposed algorithm outperforms other existing algorithms.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.4
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• pp.47-52
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• 2005

In this paper, the genetic algorithm is used to design a fixed order square decoupling $H_{\infty}$ controllers based on the Two-Degree-of-freedom standard model. The proposed decoupling $H_{\infty}$ controller which is minimizes the maximum energy in the output signal is designed to reduce the coupling properties between the input/output variables which make it difficult to control a system efficiently. A minimal set of assumptions for existence of the decoupling controller formula is described in the state-space formulas. It is verified by an example.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.299-304
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• 2018

An improved genetic algorithm was proposed for pattern synthesis of an adaptive beam forming system using phased array antennas. The proposed genetic algorithm is an algorithm that adds acquired characteristics procedure to solve local optimization using the diversity. The performance of the proposed genetic algorithm is verified through the problem of finding a suitable chromosome for a picture composed of binary. And it is confirmed that it is suitable for the adaptive beam forming system based on the performance problem of combining main beam and two pattern nulls.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.4
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• pp.251-258
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• 2018

In mathematics and computer science, an optimization problem is the problem of finding the best solution from feasible ones. In the context of genetic algorithm, the difficulty of an optimization problem can be explained in terms of problem epistasis. In biology, epistasis means that the phenotype of a gene is suppressed by one or more genes, but in an evolutionary algorithm it means the interaction between genes. In this paper, we experimentally show that problem epistasis and the performance of genetic algorithm are closely related. We compared problem epistasis (One-Max, Royal Road, and NK-Landscape) using a framework that quantifies problem epistasis based on Shannon's information theory, and could show that problem becomes more difficult as problem epistasis grows. In the case that a genetic algorithm finds the optimal solution, performance is compared through the number of generations, otherwise through the ratio of the fitness of the optimal solution to that of the best solution.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.191-199
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• 2001

This paper presents a new genetic algorithm for job-shop scheduling problems. When we design a genetic algorithm for difficult ordering problems such as job-shop scheduling problems, it is important to design encoding/crossover that is excellent in characteristic preservation and to maintain a diversity of population. We used Job-based order crossover(JOX). Since the schedules generated by JOX are not always active-schedule, we proposed a method to transform them into active schedulesby using the GT method with c)laracteristic preservation. We introduce strategies for maintaining a diversity of the population by eliminating same individuals in the population. Furthermore, we are not used mutation. Experiments have been done on two examples: Fisher s and Thompson s $lO\timeslO and 20\times5$ benchmark problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.270-275
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• 2003

많은 기업들은 그들의 생산과 배송 기능을 분리하여 각각의 최적화를 꾀하고 있는데, 이러한 접근방법에 의해 이룰 수 있는 절약은 제한적일 수밖에 없다. 최근의 공급사슬경영(SEM)에서는 이들 두 기능의 관련 비용을 총체적 관점에서 잘 조절하면서 동시에 통합 분석하는 것이 더 중요하다고 인식되고 있다. 본 논문에서는 일간지의 생산-배송계획 문제를 다룬다. 일간지는 재고로 유지될 수 없으며 고객이 정한 시간보다 늦게 배송되면 큰 손실을 야기하는 일종의 단일기간 재고품목이다. 저자들은 일간지 생산-배송의 통합 계획을 위한 혼성유전알고리듬 해법을 개발하고 예제를 이용하여 Hurter and Buer의 순차적 분리계획 해법과 성능을 비교한다. 계산실험 결과, 개발된 해법의 성능은 효과적인 것으로 나타난다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.731-734
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• 2017

GA(Genetic Algorithm)는 자연계 진화 과정의 적자생존의 유전적 부호화 및 처리과정을 모델링함으로서 해석적으로 처리하기 힘든 문제의 최적화에 널리 이용하고 있으며, 퍼지제어에서 룰의 선택에도 적용된다. 본 논문에서는 일반적인 GA방법에 자료의 유사성을 체크하는 방법을 도입하여 Fuzzy Rule선택 환경에 적용하고 시뮬레이션을 통해 이를 확인한다. 시뮬레이션 결과 제안된 SFRGA(Similarity Fuzzy Rule Genetic Algorithm)방법은 일반적 GA방법보다 단축된 지연시간 효과와 부수적으로 조기포화 현상(premature convergence)의 감소 및 자동 배정 퍼지 클리스터링(Fuzzy clustering)의 가능성을 얻을 수 있었다.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.798-800
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• 2000

전기기기의 구조 및 형상 최적화에 있어서 다양한 제한 사항과 설계방법들을 이용하기 위하여 전역 최대점과 함께 국소 최대점까지 고려할 수 있는 최적화 기법이 요구되고 있다. 다양한 제한사항들을 모두 목적 함수에 포함시킬 경우에 발생하는 여러 가지 문제점들을 해결하고 설계자의 주관적 평가도 활용할 수 있는 새로운 기법을 필요로 한다. 이처럼 다양한 해의 생성과 보존을 필요로 하는 분야에 니체(niche) 개념이 이용될 수 있다. 본 논문에서는 니체 개념을 포함하는 유전 알고리즘을 이용하여 토크의 선형성을 보장하는 토크 모터의 최적 설계를 수행하였다. 최적 설계 결과를 전역 최대점만을 찾는 최적화 기법과 비교하여 그 타당성을 입증하였다.

• Journal of Korean Society of Steel Construction
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• v.17 no.6 s.79
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• pp.661-672
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• 2005

For the efficient stochastic optimization of steel structures for which a large number of analyses is required, artificial neural networks,which have emerged as a powerful tool that could have been used to replace time-consuming procedures in many scientific or engineering applications, are applied. They are utilized for the solution of the equilibrium equations resulting from the application of the finite element method in connection with the reanalysis type of problem, for which a large number of finite element analyses are required in this study. As such, the use of artificial neural networks to predict finite element analysis outputs simplifies and facilitates the performance of the stochastic optimal design of structural systems where a trained neural network is used to replace the structural reanalysis phase. Moreover, to improve efficiency of used artificial neural networks, genetic algorithm is utilized. The stochastic optimizer used in this study is an algorithm based on the evolution theory. The efficiency of the proposed procedure is examined in problems with both volume (weight) functions and real-world cost functions