• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.333-336
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• 2002

In many cases, fuzzy modeling has a defect that the design procedure cannot be theoretically justified. To overcome this difficulty, we suggest a new design method for fuzzy model by combining genetic algorithm(GA) and mush set theory. GA, which has the advantages is optimization, and rule base. However, it is some what time consuming, so are introduce rough set theory to the rule reduction procedure. As a result, the decrease of learning time and the considerable rate of rule reduction is achieved without loss of useful information. The preposed algorithm is composed of three stages; First stage is quasi-optimization of fuzzy model using GA(coarse tuning). Next the obtained rule base is reduced by rough set concept(rule reduction). Finally we perform re-optimization of the membership functions by GA(fine tuning). To check the effectiveness of the suggested algorithm, examples for time series prediction are examined.

• 한국정밀공학회지
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• 제14권3호
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• pp.89-97
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• 1997

A genetic algorithm (GA) is a stochastic direct search strategy that mimics the process of genetic evolution. The GA applied herein works on a population of structural designs at any one time, and uses a structured information exchange based on the principles of natural selection and wurvival of the fittest to recombine the most desirable features of the designs over a sequence of generations until the process converges to a "maximum fitness" design. Principles of genetics are adapted into a search procedure for structural optimization. The methods consist of three genetics operations mainly named selection, cross- over and mutation. In this study, a method of finding the optimum topology of truss/beam structure is pro- posed by using the GA. In order to use GA in the optimum topology problem, chromosomes to FEM elements are assigned, and a penalty function is used to include constraints into fitness function. The results show that the GA has the potential to be an effective tool for the optimal design of structures accounting for sizing, geometrical and topological variables.variables.

• 한국전자통신학회논문지
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• 제8권11호
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• pp.1665-1674
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• 2013

본 논문에서는 허리 관절을 갖는 4족 로봇의 효율적인 경사면 보행을 위해 유전 알고리듬(Genetic Algorithm: GA)을 이용한 경사면 보행 방법을 제안한다. 제안한 방법에서는 먼저, 허리 관절을 갖는 4족 로봇의 기구학적 모델을 유도하며, GA를 수행하기 위한 유전자 및 적합도 함수를 설정한다. 또한, 경사면에서 최적의 에너지 안정여유도(Energy Stability Margin: ESM)를 갖는 4족 로봇의 자세와 도달 영역 내의 발끝 착지 지점을 GA를 이용하여 자동으로 탐색하여 보행한다. 마지막으로, 4족 보행 로봇의 모의 실험을 통해 기존 방법과 비교함으로써 본 논문에서 제안한 방법의 효용성을 검증한다.

• 전력전자학회논문지
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• 제2권4호
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• pp.45-55
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• 1997

FA의 중요성이 증가함에 따라 AGV(Autonomous Guided Vehicle)의 역할 또한 중요시되고 있다. 본 논문은 인공 지능의 여러 방법론을 통합하여 하이브리드 형태의 제어기가 가질 수 있는 상호 보완적인 특징을 이용하고자 하며, 유전자 알고리즘에 의한 자기조직이 가능한 퍼지제어기로써 능동적이고 효과적인 AGV 제어기를 구성하고자 한다. 자기 조직이 가능한 퍼지 제어기는 구성하기 위하여 GA(Genetic Algorithm)를 사용하여 멤버십 함수와 제어 규칙을 최적에 근사하게 튜닝하였으며 제어 규칙의 자기 수정 또는 생성을 통하여 제어 성능을 향상시킨다.

• 인터넷정보학회논문지
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• 제6권6호
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• pp.99-106
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• 2005

멀티프로세서 시스템에서 스케줄링은 매우 중요한 부분이지만, 최적의 해를 구하는 것이 복잡하여 다양한 휴리스틱 방법들에 의한 스케줄링 알고리즘들이 제안되고 있다. 최근 유전 알고리즘을 사용한 멀티프로세서 스케줄링 알고리즘들이 제시되고 있지만, 제시된 알고리즘 대부분은 한가지만의 목적을 가지는 단순한 알고리즘이다. 본 논문에서는 유전 알고리즘을 이용한 새로운 스케줄링 알고리즘을 제시한다. 또한, 해를 구하는 과정에서 시뮬레이티드 어닐링 (simulated annealing)의 확률을 이용하여 유전 알고리즘의 성능을 개선시킨다. 제시된 알고리즘은 태스크들의 최종 수행 완료 시간 (makespan)을 최소화하는 것과 사용된 프로세서의 수를 최소화하는 두 가지의 목표를 가진다. 모의 실험을 통하여 제시된 알고리즘이 다른 알고리즘보다 최종 수행 완료 시간과 사용된 프로세서의 수에서 더 나은 결과를 보임을 확인할 수 있었다.

• 산업경영시스템학회지
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• 제40권4호
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• pp.260-267
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• 2017

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.

• 한국정보통신학회논문지
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• 제3권2호
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• pp.383-388
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• 1999

본 논문에서는 정밀 제어와 온-라인 제어를 위하여 유전 알고리즘을 이용한 퍼지-신경망 제어기를 제안하였다. 제안된 제어기의 설계방법은 유전 알고리즘을 사용하여 불확실한 플랜트에 대한 근사적 퍼지 소속함수를 얻은 후, 퍼지-신경망 제어기의 적응학습에 의해 최적의 퍼지 소속함수를 조정할 수 있는 제어구조를 제안하였다. 제안된 제어기를 사용했을 때의 효율성과 정확성을 평가하기 위하여 DC 서보모터의 속도제어 실험을 통해 GA-Fuzzy 제어기를 사용했을 때와 비교분석 한다.

• 한국건설관리학회논문집
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• 제8권6호
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• pp.235-245
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• 2007

This research study presents the development and application of an Elitist Genetic Algorithm (Elitist GA) for solving the resource-constrained project scheduling problem, which is one of the most challenging problems in construction engineering. Main features of the developed algorithm are that the elitist roulette selection operator is developed to preserve the best individual solution for the next generation so as to obtain the improved solution, and that parallel schedule generation scheme is used to generate a feasible solution to the problem. The experimental results on standard problem sets indicate that the proposed algorithm not only produces reasonably good solutions to the problems over the heuristic method and other GA, but also can find the optimal and/or near optimal solutions for the large-sized problems with multiple resources within a reasonable amount of time that will be applicable to the construction industry. This paper will help researchers and/or practitioners in the construction project scheduling software area with alternative means to find the optimal schedules by utilizing the advantages of the Elitist GA.

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• 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.