• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.285-289
• /
• 1992

This paper describes a new optimization technique for the design of traffic signal patterns. The proposed method uses a Genetic Algorithm for searching through the better signal patterns. Since the Genetic Algorithm is effective to search directly through a huge binary coded state spaces, the proposed design method has the following advantages over the conventional OR methods: (1) on-line optimization is available within a reasonable time, (2) there is no limitation to the types of signals to be optimized. Some computer simulations are carried out and its ability of getting high quality control in a short period is demonstrated.

• Economic and Environmental Geology
• /
• v.28 no.4
• /
• pp.425-431
• /
• 1995

Genetic algorithms are so named because they are analogous to biological processes. The model parameters are coded in binary form. The algorithm then starts with a randomly chosen population of models called chromosomes. The second step is to evaluate the fitness values of these models, measured by a correlation between data and synthetic for a particular model. Then, the three genetic processes of selection, crossover, and mutation are performed upon the model in sequence. Genetic algorithms share the favorable characteristics of random Monte Carlo over local optimization methods in that they do not require linearizing assumptions nor the calculation of partial derivatives, are independent of the misfit criterion, and avoid numerical instabilities associated with matrix inversion. An additional advantage over converntional methods such as iterative least squares is that the sampling is global, rather than local, thereby reducing the tendency to become entrapped in local minima and avoiding the dependency on an assumed starting model.

• Journal of Industrial Technology
• /
• v.29 no.B
• /
• pp.9-15
• /
• 2009

In the case of traditional binary encoding technique, it takes long time to converge the optimal solutions and brings about complexity of the systems due to encoding and decoding procedures. However, the ROGAs (real-coded genetic algorithms) do not require these procedures, and the k-means clustering algorithm can avoid global searching space. Thus, this paper proposes a new approach by using their advantages. The proposed method constructs the multiple predictors using the optimal differences that can reveal the patterns better and properties concealed in non-stationary time series where the k-means clustering algorithm is used for data classification to each predictor, then selects the best predictor. After selecting the best predictor, the cluster centers of the predictor are tuned finely via RCGKA in secondary tuning procedure. Therefore, performance of the predictor can be more enhanced. Finally, we verifies the prediction performance of the proposed system via simulating typical time series examples.

• Proceedings of the KIEE Conference
• /
• 1993.07b
• /
• pp.923-928
• /
• 1993

Genetic algorithms are powerful optimization methods based on the mechanism of natural genetics and natural selection. Genetic algorithms reduce chance of searching local optima unlike most conventional search algorithms and especially show good performances in complex nonlinear optimization problems because they do not require any information except objective function value. This paper presents a new model based on sexual reproduction in nature. In the proposed Sexual Reproduction model(SR model), individuals consist of the diploid of chromosomes, which are artificially coded as binary string in computer program. The meiosis is modeled to produce the sexual cell(gamete). In the artificial meiosis, crossover between homologous chromosomes plays an essential role for exchanging genetic informations. We apply proposed SR model to optimization of the design parameters of Single-sided Linear Induction Motor(SLIM). Sequential Unconstrained Minimization Technique(SUMT) is used to transform the nonlinear optimization problem with many constraints of SLIM to a simple unconstrained problem, We perform optimal design of SLIM available to FA conveyer systems and discuss its results.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.711-713
• /
• 1996

This paper proposes a unit commitment scheduling method based on Genetic Algorithm(GA). Due to a variety of constraints to be satisfied, the search space of the UC problem is highly nonconvex, so the UC problem cannot be solved efficiently only using the standard GA To efficiently deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up and down time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of other constraints arc handled by integrating penalty factors. To show the effectiveness of the GA based unit commitment scheduling, test results for system of 5 units are compared with results obtained using Lagrangian Relaxation and Dynamic Programming.

• Journal of the Korean Institute of Telematics and Electronics D
• /
• v.36D no.7
• /
• pp.56-65
• /
• 1999

In this paper, a pixelated binary phase grating to generate $5{\times}5$ spots in designed using simple Genetic Algorithm(sGA) composed of selection, crossover, and mutation operators, and it can be applied for the optical interconnection. So as to adapt that GA is a robust and efficient schema, a chromosome is coded as a binary integer of length $32{\times}32$, the ranking method for decreasing the stochastic sampling error is performed, and a single-point crossover having $16{\times}16$ block size is used. A designed grating when the probabillty of mutation is 0.001, the probability of crossover is 0.75 and the population size is 300 has a 74.7[%] high diffraction efficiency and a $1.73{\times}10^{-1}$ uniformity quantitatively.