• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.106.4-106
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• 2001

The current research field to find near-optimum solutions explores in a small population, which is coined as Micro-Genetic Algorithms (${\mu}$GAs), with some genetic operators. Just as in the Simple-Genetic Algorithms (SGAs), the ${\mu}$GAs work with encoding population and are implemented serially. The major difference between SGAs and ${\mu}$GAs is how to make reproductive plan for more better searching strategy due to the population choice. This paper is conducted to implement ${\mu}$GAs in order to achieve fast searching for more better evolution and associated cost evaluation in global solution space. To achieve this implementation, the Air-Borne Selection (ABS) for a new reproductive plan is developed as new strategic conception for ${\mu}$GAs. In this paper, it is shown that the ${\mu}$GAs implementation reaches a near-optimal region much earlier than the SGAs implementation. The superior ...

• 제어로봇시스템학회논문지
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• 제6권4호
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• pp.268-275
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• 2000

The increasing technological demands of today call for complex systems, which in turn involve a series of optimization problems with some equality or inequality constraints. In this paper, we presents a real-coded genetic algorithm(RCGA) as an optimization tool which is implemented by three genetic operators based on real coding representation. Through a lot of simulation works, the optimum settings of its control parameters are obtained on the basis of global off-line robustness for use in off-line applications. Two optimization problems are Presented to illustrate the usefulness of the RCGA. In case of a constrained problem, a penalty strategy is incorporated to transform the constrained problem into an unconstrained problem by penalizing infeasible solutions.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.452-455
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• 1999

We introduce an evolutionary approach to on-line learning for mobile robot control using reconfigurable hardware. We use genetic programming as an evolutionary engine. Control programs are encoded in tree structure. Genetic operators, such as node mutation, adapt the program trees based on a set of training cases. This paper discusses the advantages and constraints of the evolvable hardware approach to robot learning and describes a FPGA implementation of the presented genetic programming method.

• 상하수도학회지
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• 제18권3호
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• pp.312-319
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• 2004

The optimum dosage control is presumably the goal of every water treatment plant. However it is difficult to determine the dosage rate of coagulant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the real number genetic-fuzzy system was used in determining the dosage rate of the coagulant. The genetic algorithms are excellently robust in complex optimization problems. Since it uses randomized operators and searches for the best chromosome without auxiliary informations from a population which consists of codings of parameter set. To apply this algorithms, we made the real number rule table and membership function from the actual operation data of the water treatment plant. We determined optimum dosages of coagulant(LAS) using the fuzzy operation and compared them with the dosage rate of the actual operation data.

• 대한기계학회논문집A
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• 제33권8호
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• pp.786-792
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• 2009

Topology optimization is to find the optimal material distribution of the specified design domain minimizing the objective function while satisfying the design constraints. Since the genetic algorithm (GA) has its advantage of locating global optimum with high probability, it has been applied to the topology optimization. To guarantee the structural connectivity, the concept of compliance pattern is proposed and to improve the convergence rate, small number of population size and variable probability in genetic operators are incorporated into GA. The rank sum weight method is applied to formulate the fitness function consisting of compliance, volume, connectivity and checkerboard pattern. To substantiate the proposed method design examples in the previous works are compared with respect to the number of function evaluation and objective function value. The comparative study shows that the compliance pattern based GA results in the reduction of computational cost to obtain the reasonable structural topology.

• 한국경영과학회지
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• 제14권2호
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• pp.47-47
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• 1989

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• International Journal of Aeronautical and Space Sciences
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• 제14권4호
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• pp.341-349
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• 2013

This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.

• Steel and Composite Structures
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• 제4권6호
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• pp.453-469
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• 2004

In this article, a genetic algorithm based optimum design method is presented for non-linear steel frames with semi-rigid connections. The design algorithm obtains the minimum weight frame by selecting suitable sections from a standard set of steel sections such as European wide flange beams (i.e., HE sections). A genetic algorithm is employed as optimization method which utilizes reproduction, crossover and mutation operators. Displacement and stress constraints of Turkish Building Code for Steel Structures (TS 648, 1980) are imposed on the frame. The algorithm requires a large number of non-linear analyses of frames. The analyses cover both the non-linear behaviour of beam-to-column connection and $P-{\Delta}$ effects of beam-column members. The Frye and Morris polynomial model is used for modelling of semi-rigid connections. Two design examples with various type of connections are presented to demonstrate the application of the algorithm. The semi-rigid connection modelling results in more economical solutions than rigid connection modelling, but it increases frame drift.

• Journal of Electrical Engineering and information Science
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• 제2권5호
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• pp.51-59
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• 1997

This paper presets a methodology of improving a conventional model in power systems using Genetic Algorithms(GAs) and suggests a GAs-based model which can directly solve the real-valued optimum in an optimization procedure. In applying GAs to the power flow, a new fitness mapping method is proposed using the proposed using the probability distribution function for all the payoffs in the population pool. In this approach, both the notions on a way of the genetic representation, and a realization of the genetic operators are fully discussed to evaluate he GAs' effectiveness. The proposed method is applied to IEEE 5-bus, 14-bus and 25-bus systems and, the results of computational experiments suggest a direct applicability of GAs to more complicated power system problems even if they contain nonlinear algebraic equations.

• 지능정보연구
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• 제13권3호
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• pp.101-117
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• 2007

본 논문에서는 연결성, 신뢰성, 교통량 제약조건을 만족하는 최소 비용의 소형궤도차량 선로네트워크를 설계하기 위한 유전알고리즘을 제안한다. 소형궤도차량 시스템은 다수의 자동화된 차량들이 공중에 설치된 선로네트워크를 따라 움직이는 신개념 교통 시스템이다. 주어진 역의 위치와 역간 교통량 수요에 대해 최적의 선로네트워크를 구하는 문제는 소형궤도차량 시스템에 관련된 가장 중요한 문제 가운데 하나이다. 본 논문에서는 선로네트워크를 방향성 링크를 갖는 그래프로 표현하고 그 비용과 연결성, 신뢰성, 교통량을 수식화하였다. 이렇게 주어진 네트워크 성능지표를 바탕으로 선로네트워크 설계 문제에 적절한 연산자들로 구성된 유전알고리즘을 제안한다. 이 연산자들은 안정상태 선택 연산자, 수리 알고리즘, 방향성이 있는 돌연변이 연산자를 포함한다. 제안된 유전알고리즘의 적절한 변수를 결정하고 그 성능을 타 알고리즘과 비교하기 위한 실험을 수행하였다. 최대 210개의 링크를 갖는 선로네트워크에 대해 수행된 실험결과는 제안된 유전알고리즘이 적절한 시간 내에 만족스러운 해를 구할 수 있음을 보인다.