• 제어로봇시스템학회논문지
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• 제17권12호
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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.

• 한국항행학회논문지
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• 제18권2호
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• pp.170-177
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• 2014

유전 알고리즘은 강인한 탐색과 최적화 기술이기는 하나 조기 수렴과 국부 최적해에 수렴하는 문제점들을 내포하고 있다. 모집단의 다양성이 작은 값으로 수렴할수록 탐색능력이 감소하고, 국부 최적해에 수렴하지만, 모집단의 다양성이 높은 값으로 수렴할수록 탐색능력이 증가하고 전역 최적해에 수렴할 수 있으나 유전 알고리즘은 발산할 수도 있다. 유전 알고리즘이 전역 최적해에 수렴하는 것을 보장하기 위해서는 유전 연산자가 적절하게 선정되어야 한다. 본 논문에서는 조기 수렴으로부터 벗어나기 위하여 모집단의 다양성을 유지하도록 평균해밍거리와 적합도 값을 혼합한 함수를 이용한 유전 연산자들을 제안하였다. 모의실험을 통하여 다양성의 유지를 위한 돌연변이 연산자와 수렴 특성의 향상을 위한 다른 유전자들의 효과를 확인할 수 있었으며, 본 논문에서 제안한 유전 연산자들이 조기 수렴이나 국부 최적해에 수렴하는 경우를 피하는데 유용한 방법임이 확인되었다.

• 대한전기학회논문지:전력기술부문A
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• 제48권5호
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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.

• Computers and Concrete
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• 제11권6호
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• pp.541-569
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• 2013

There is a wide variety of existing Genetic Algorithms (GA) operators and parameters in the literature. However, there is no unique technique that shows the best performance for different classes of optimization problems. Hence, the evaluation of these operators and parameters, which influence the effectiveness of the search process, must be carried out on a problem basis. This paper presents a comparison for the influence of GA operators and parameters on the performance of the damage identification problem using the finite element model updating method (FEMU). The damage is defined as reduction in bending rigidity of the finite elements of a reinforced concrete beam. A certain damage scenario is adopted and identified using different GA operators by minimizing the differences between experimental and analytical modal parameters. In this study, different selection, crossover and mutation operators are compared with each other based on the reliability, accuracy and efficiency criteria. The exploration and exploitation capabilities of different operators are evaluated. Also a comparison is carried out for the parallel and sequential GAs with different population sizes and the effect of the multiple use of some crossover operators is investigated. The results show that the roulettewheel selection technique together with real valued encoding gives the best results. It is also apparent that the Non-uniform Mutation as well as Parent Centric Normal Crossover can be confidently used in the damage identification problem. Nevertheless the parallel GAs increases both computation speed and the efficiency of the method.

• Journal of Biosystems Engineering
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• 제20권1호
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• pp.87-94
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• 1995

When farmer loads and unloads farm products with a trailer, linked to a tractor, the tractor-trailer is backed up to the loading duck. However, travelling backward is not easy and takes a time for even skilled operators. Therefore, unmanned backing up is necessary to save the effort. A backward controller of tractor-trailer was simulated using fuzzy logic and genetic algorithms. Operators drive the tractor-trailer back and forth several times for backing up to the loading duck. As the operators did it, a backward controller was designed using fuzzy logic. And genetic algorithms was applied to improve the performance of the backward controller. With the strings coded with the fuzzy membership functions, genetic operations were carried out. After 30 generations, the best fitted fuzzy membership functions were found. Those membership functions were used in the fuzzy backward controller. The fuzzy controller combined with genetic algorithms showed the better results than the fuzzy controller did alone.

• 경영과학
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• 제20권1호
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• pp.51-64
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• 2003

The JSSP (Job Shop Scheduling Problem) Is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. we design scheduling method based on SGA (Single Genetic Algorithm) and PGA (Parallel Genetic Algorithm). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling method based on genetic algorithm are tested on five standard benchmark JSSPs. The results were compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement at a solution. The superior results indicate the successful Incorporation of generating method of initial population into the genetic operators.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.294-298
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• 2008

이메본 논문은 GP 트리의 노드포화도를 제어함으로써 트리의 구조공간에서 효율적인 개체 분포를 유도하는 GP 진화연산자를 제안한다. 특정 영역으로의 트리 개체의 분포가 성능에 미치는 영향을 검증하고 진화과정에서 나타나는 군집내의 개체 다양성과의 관계를 분석한다. 제안된 진화연산자를 회귀다항식, 멀티플렉서, 짝수 패리티의 3가지 벤치마크 문제에 대해서 실험을 하였고, 표준 GP 연산자와 비교하였다.

• 산업공학
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• 제17권2호
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• pp.242-248
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• 2004

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots to allow the overlapping of operations between successive machines in a multi-stage production system. A new genetic algorithm (NGA) is proposed for minimizing the mean weighted absolute deviation of job completion times from due dates when jobs are scheduled in a no-wait lot-streaming flow shop. In a no-wait flow shop, each sublot must be processed continuously from its start in the first machine to its completion in the last machine without any interruption on machines and without any waiting in between the machines. NGA replaces selection and mating operators of genetic algorithms (GAs), which often lead to premature convergence, by new operators (marriage and pregnancy operators) and adopts the idea of inter-chromosomal dominance. The performance of NGA is compared with that of GA and the results of computational experiments show that NGA works well for this type of problem.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.646-651
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• 1998

Fuzzy control has been used successfully in many practical applications. In traditional methods, experience and control knowledge of human experts are needed to design fuzzy controllers. However, it takes much time and cost. In this paper, an automatic design method for fuzzy controllers using genetic algorithms is proposed. In the method, we proposed an effective encoding scheme and new genetic operators. The maximum number of linguistic terms is restricted to reduce the number of combinatorial fuzzy rules in the research space. The proposed genetic operators maintain the correspondency between membership functions and control rules. The proposed method is applied to a cart centering problem. The result of the experiment has been satisfactory compared with other design methods using genetic algorithms.

• Computers and Concrete
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• 제22권5호
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• pp.493-500
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• 2018

Due to the fact that the ratio of their height to their openings is very large compared to normal beams, there are difficulties in the design and analysis of deep beams, which differ in behavior. In this study, the optimum horizontal and vertical reinforcement diameters of 5 different beams were determined by using genetic algorithms (GA) due to the openness/height ratio (L/h), loading condition and the presence of spaces in the body. In this study, the effect of different mutation operators and improved double times sensitive mutation (DTM) operator on GA's performance was investigated. In the study following random mutation (RM), boundary mutation (BM), non-uniform random mutation (NRM), Makinen, Periaux and Toivanen (MPT) mutation, power mutation (PM), polynomial mutation (PNM), and developed DTM mutation operators were applied to five deep beam problems were used to determine the minimum reinforcement diameter. The fitness values obtained using developed DTM mutation operator was higher than obtained from existing mutation operators. Moreover; obtained reinforcement weight of the deep beams using the developed DTM mutation operator lower than obtained from the existing mutation operators. As a result of the analyzes, the highest fitness value was obtained from the applied double times sensitive mutation (DTM) operator. In addition, it was found that this study, which was carried out using GAs, contributed to the solution of the problems experienced in the design of deep beams.