• 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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• 2001.04a
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• pp.74-78
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• 2001

In this study, a multi-step optimization technique combined with a simple genetic algorithm is introduce to the structural design optimization of a high speed machining center. In this case, the design problem is to find out the best design variables which minimize the static compliance, the dynamic compliance, and the weight of the machine structure and meet some design constraints simultaneously. Dimensional thicknesses of the thirteen structural members along the static force loop of the machine structure are adopted as design variables. The first optimization step is a static design optimization, in which the static compliance and the weight are minimized under some dimensional and safety constraints. The second step is a dynamic design optimization, where the dynamic compliance and the weight are minimized under the same constraints. After optimization, the weight of the moving body was reduced to 9.1% of the initial design respectively. Both static and dynamic compliances of the optimum design are also in the feasible range even thought they were slightly increased than before.

• Journal of Microbiology and Biotechnology
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• v.11 no.5
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• pp.739-748
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• 2001

A self-organizing fuzzy logic controller using a genetic algorithm is described, which controlled the glucose concentration for the enhancement of flavonoid production in a fed-batch cultivation of Scutellaria baicalensis G. plant cells. The substrate feeding strategy in a fed-batch culture was to increase the flavonoid production by using the proposed kinetic model. For the two-stage culture, the substrate feeding strategy consisted of a first period with 28 g/I of glucose to promote cell growth, followed by a second period with 5 g/I of glucose to promote flavonoid production. A simple fuzzy logic controller and the self-organizing fuzzy logic controller using a genetic algorithm was constructed to control the glucose concentration in a fed-batch culture. The designed fuzzy logic controllers were applied to maintain the glucose concentration at given set-points of the two-stage culture in fed-batch cultivation. The experimental results showed that the self-organizing fuzzy logic controller improved the controller\`s performance, compared with that of the simple fuzzy logic controller. The specific production yield and productivity of flavonoids in the two-stage culture were higher than those in the batch culture.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.69-80
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• 1999

A genetic algorithm to determine the optimal signal sequence and double cycle pattern is described. The signal sequence and double cycle pattern are used as the input for TRANSYT to find optimal signal timing at each junction in the area traffic networks, In the genetic process, the partially matched crossover and simple crossover operators are used for evolution of signal sequence and double cycle pattern respectively. A special conversion algorithm is devised to convert the signal sequence into the link-stage assignment for TRANSYT. Results from tests using data from an area traffic network in Leicester region R are given.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.744-746
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• 2000

This paper presents an optimum design of a linear induction motor(LIM) using genetic algorithm(GA). Sequential unconstrained minimization technique(SUMT) is used to transform the nonlinear optimization with constraints to a simple unconstrained problem. The objective functions of LIM such as trust, weight are optimized and the result was applied to the design of linear induction pump.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.463-464
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• 2007

This paper presents the hardware-based genetic algorithm, written in VHDL. Due to parallel computation and no function call overhead, a hardware-based GA advantage a speedup over a software-based GA. The proposed architecture is constructed on a field-programmable gate arrays, which are easily reconfigured. Since a general-purpose GA requires that the fitness function be easily changed, the hardware implementation must exploit the reprogrammability.

• Journal of Korean Society of Steel Construction
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• v.13 no.5
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• pp.577-586
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• 2001

The objective of this study is the development of sizing and system shape discrete optime design algorithm which is based on the genetic algorithms (GAs). The algorithm can perform both size and shape optimum designs of space trusses. The developed algorithm was implemented in a computer program. The algorithm is known to be very efficient for the discrete optimization The genetic process selects the next design points based on the survivability of the current design points The evolutionary process evaluates the survivability of the design points selected from the genetic process in the genetic process of the simple genetic algorithms there are three basic operators : reproduction cross-over and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.369-376
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• 2007

This paper proposes a hybrid genetic algorithm(HGA) for the classifier ensemble selection. HGA is added a local search operation for increasing the fine-turning of local area. This paper apply hybrid and simple genetic algorithms(SGA) to the classifier ensemble selection problem in order to show the superiority of HGA. And this paper propose two methods(SSO: Sequential Search Operations, CSO: Combinational Search Operations) of local search operation of hybrid genetic algorithm. Experimental results show that the HGA has better searching capability than SGA. The experiments show that the CSO considering the correlation among classifiers is better than the SSO.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.25-30
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• 1997

Genetic Algorithm(GA) is a parallel, global search technique modeled with the Darwinian principle of survival and reproduction of the fittest. Since Holland has proposed GA called the Simple GA, considerable research has focused of improving Simple GA performance. In this paper, I describe some methods of GA's modeling in different field.

• Journal of Ocean Engineering and Technology
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• v.18 no.5
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• pp.29-35
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• 2004

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is proposed for identifying a global solution, using continuous zooming factors for optimization problems. In order to improve the local fine-tuning of the GA, we introduced a new method whereby the search space is zoomed around the design variable with the best fitness per 100 generation, resulting in an improvement of the convergence. Furthermore, the reliability of the optimized solution is determined based on the theory of probability, and the parameter used for the successive zooming method is optimized. With parameter optimization, we can eliminate the time allocated for deciding parameters used in SZGA. To demonstrate the superiority of the proposed theory, we tested for the minimization of a multiple function, as well as simple functions. After testing, we applied the parameter optimization to a truss problem and wicket gate servomotor optimization. Then, the proposed algorithm identifies a more exact optimum value than the standard genetic algorithm.