• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6C
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• pp.480-486
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• 2008

The high peak-to-average power ratio (PAPR) of the transmitted signals is one of major drawbacks of the orthogonal frequency division multiplexing (OFDM). A partial transmit sequences (PTS) technique can improve the PAPR statistics of OFDM signals. However, in a PTS technique, the search complexity to select phase weighting factors increases exponentially with the number of sub-blocks. In this paper, a PTS technique with low computational complexity is presented, which adopts micro-genetic algorithm(${\mu}$-GA) as a search algorithm. A search on the phase weighting factors starts with a population of five randomly generated individuals. An elite having the largest fitness value and the other four individuals selected through the tournament selection strategy are determined, and then the next generation members are generated through the crossover operations among those. If the new generation converges, all the four individuals except the elite are randomly generated again. The search terminates when there has been no improvements on the PAPR during the predefined number of generations, or the maximum number of generations has been reached. To evaluate the performance of the proposed PTS technique, the complementary cumulative distribution functions (CCDF) of the PAPR are compared with those of the conventional PTS techniques.

• Transactions of Materials Processing
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• v.5 no.4
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• pp.269-280
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• 1996

A new approach to die shape optimal design in extrusion is presented. The approach consists of a FEM analysis model to predict the value of the objective function a design model to relate the die profile with the design variables and a genetic algorithm based optimaization procedure. The approach was described in detail with emphasis on our modified micro genetic algorithm. Comparison with theoretical solutions was made to examine the validity of the predicted optimal die shapes. The approach was then applied to revealing the optimal die shapes with regard to various objective functions including those for which the design sensitivities can not be deter-mined analytically.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.161-167
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• 2008

In this paper, the optimal design of trusses using advanced analysis and genetic algorithm is performed. An advanced analysis takes into account geometric nonlinearity and material nonlinearity. The micro genetic algorithm is used as optimization technique. The weight of structures is treated as the objective function. The constraint functions are defined by load-carrying capacities and displacement requirement. The effectiveness of the proposed method is verified by comparing the results of the proposed method with those of other method.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.853-858
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• 1993

The error back-propagation(BP) algorithm is widely used for finding optimum weights of multi-layer neural networks. However, the critical drawback of the BP algorithm is its slow convergence of error. The major reason for this slow convergence is the premature saturation which is a phenomenon that the error of a neural network stays almost constant for some period time during learning. An inappropriate selections of initial weights cause each neuron to be trapped in the premature saturation state, which brings in slow convergence speed of the multi-layer neural network. In this paper, to overcome the above problem, Micro-Genetic algorithms(.mu.-GAs) which can allow to find the near-optimal values, are used to select the proper weights and slopes of activation function of neurons. The effectiveness of the proposed algorithms will be demonstrated by some computer simulations of two d.o.f planar robot manipulator.

• Structural Engineering and Mechanics
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• v.23 no.4
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• pp.387-407
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• 2006

A Genetic Algorithm (hereinafter GA) based optimum design algorithm and program for plane steel frames with partially restrained connections is presented. The algorithm was incorporated with the refined plastic hinge analysis method, in which geometric nonlinearity was considered by using the stability functions of beam-column members and material nonlinearity was considered by using the gradual stiffness degradation model that included the effects of residual stress, moment redistribution by the occurrence of plastic hinges, partially restrained connections, and the geometric imperfection of members. In the genetic algorithm, a tournament selection method and micro-GAs were employed. The fitness function for the genetic algorithm was expressed as an unconstrained function composed of objective and penalty functions. The objective and penalty functions were expressed, respectively, as the weight of steel frames and the constraint functions which account for the requirements of load-carrying capacity, serviceability, ductility, and construction workability. To verify the appropriateness of the present method, the optimum design results of two plane steel frames with fully and partially restrained connections were compared.

• Proceedings of the KWS Conference
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• 2007.11a
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• pp.57-59
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• 2007

• Journal of Navigation and Port Research
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• v.30 no.2
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• pp.137-143
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• 2006

The optimum grillage design belongs to nonlinear constrained optimization problem. The determination of beam scantlings for the grillage structure is a very crucial matter out of whole structural design process. The performance of optimization methods, based on penalty functions, is highly problem-dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm ($R{\mu}GA$) is proposed to find the optimum beam scantlings of the grillage structure without handling any of penalty functions. $R{\mu}GA$ can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. Direct stiffness method is used as a numerical tool for the grillage analysis. In optimization study to find minimum weight, sensitivity study is carried out with varying beam configurations. From the simulation results, it has been concluded that the proposed $R{\mu}GA$ is an effective optimization tool for solving continuous and/or discrete nonlinear real-world optimization problems.

• Journal of Korean Society of Steel Construction
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• v.17 no.2 s.75
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• pp.173-181
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• 2005

This study deals with a method to identify damages of free vibrating thin plate structures using the combined finite element method (FEM) and the advanced uniform micro-genetic algorithm.To solve the inverse problem using the combined method, this study uses several natural frequencies instead of mode shapes in a structure as the measured data. The technique described in this paper allows us not only to detect the damaged elements but also to find their numbers, locations, and the extent of damage.To demonstrate the feasibility of the proposed method, the algorithm is applied to a free vibrating steel thin plate structures with arbitrary damages. From the standpoint of computation efficiency, the proposed method in this study has advantages when compared with the existing simple genetic algorithms. The numerical examples demonstrate that the method using micro-genetic algorithms can possibly detect correctly the damages of thin plates from only several natural frequencies instead of their natural modes.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.416-421
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• 2008

A Metropolis genetic algorithm (MGA) is a newly-developed hybrid algorithm combining simple genetic algorithm (SGA) and simulated annealing (SA). In the algorithm, favorable features of Metropolis criterion of SA are incorporated in the reproduction operations of SGA. This way, MGA alleviates the disadvantages of finding imprecise solution in SGA and time-consuming computation in SA. It has been successfully applied and the efficiency has been verified for the practical structural design optimization. However, applicability of MGA for the wider range of problems should be rigorously proved through the solution of mathematical optimization problems. Thus, performances of MGA for the typical mathematical problems are investigated and compared with those of conventional algorithms such as SGA, micro genetic algorithm (${\mu}GA$), and SA. And, for better application of MGA, the effects of acceptance level are also presented. From numerical Study, it is again verified that MGA is more efficient and robust than SA, SGA and ${\mu}GA$ in the solution of mathematical optimization problems having various features.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.608-611
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• 2003

In this paper, laser direct micromaching is developed to fabricate micro patterns using UV laser ( λ$_3$= 355 nm). Experimentally, laser beam paths mainly influences the surface shape quality. Thus. we proposed laser beam path generator by extracting shape data in a blueprint worked through CAD modeler and using genetic algorithm that considers the characteristics of laser beam. The results show that various shapes of micro patterns could be manufactured using proposed method in this paper.