• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1113-1120
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• 2004

This paper proposes a novel hybrid genetic algorithm for the feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of the fine-tuning power, and their effectiveness and timing requirement are analyzed and compared. Experimentations performed with various standard datasets revealed that the proposed hybrid GA is superior to a simple GA and sequential search algorithms.

• 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.

• 한국정보통신설비학회:학술대회논문집
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• 2004.08a
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• pp.302-307
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• 2004

이 논문은 제한된 대역폭을 가진 고정 채널 할당 문제를 유전알고리즘을 이용하여 최적화하는 방법을 제시한다. 또한 이 논문에서는 유전알고리즘의 미세조정 능력 향상을 위해 지역 최적화 방법을 고안하였다. 고정채널 할당 문제를 행렬로 표현하여 지역 최적화 알고리즘에서는 채널을 수평 또는 수직으로 움직임으로써 유전 알고리즘의 성능 향상을 꾀한다. 제안된 방법은 기존의 방법보다 뛰어난 성능 향상을 나타냄을 실험결과에서 보일 것이다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.64-68
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• 1995

미세드릴가공은 최근의 공업제품의 소형 경량화 추세로 인해 수요가 급증하고 있으나 가공시에 있어서 많은 난 점이 존재 하기 때문에 강도 높은 가공기와 숙련된 가공전문가를 필요로 한다. 본 연구에서는 미세드릴가공을 수행하기 위해 우선 절삭상태 검출방법으로써 실용적이고 가공상황에 간섭을 일으키지 않는 주축용 모터의 전류 값을 이용하며 제어기 설계를 위해 퍼지추론과 유전알고리즘 이론을 도입한다. 이러한 지능형 가공방법을 미세 드릴가공에 구현하기 위해서 오프라인으로 안정한 가공조건을 초기화한 다음 퍼지제어기를 이용하여 일정한 절삭력을 유지할 수 있도록 실시간으로 이송속도를 제어하며 가공상황 변동에 따른 적절한 퍼지규칙을 자기 동조하는 최적화 알고리즘을 제안한 후 실제가공을 통하여 미세드릴가공의 특성과 제어기의 성능을 평가한다.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.51-58
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• 2004

Optimization of engine desist and operation parameters using a genetic algorithm was demonstrated for direct injection diesel engine combustion. A micro genetic algorithm and a modified KIVA-3V code were used for the analysis and optimization of the engine combustion. At each generation of the optimization step the micro genetic algorithm generated five groups of parameter sets, and the five cases of KIVA-3V analysis were to be performed either in series or in parallel. The micro genetic algorithm code was also parallelized by using MPI programming, and a multi-CPU parallel supercomputer was used to speed up the optimization process by four times. An example case for a fixed engine speed was performed with six parameters of intake swirl ratio, compression ratio, fuel injection included angle, injector hole number, SOI, and injection duration. A simultaneous optimization technique for the whole range of engine speeds would be suggested for further studies.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.2
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• pp.101-107
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• 2005

In this paper, optimized operating parameters were found using multi-dimensional engine simulation software (KIVA-3V) and micro-genetic algorithm for heavy duty diesel engine. The engine operating condition considered was at 1,737 rev/min and 57 % load. Engine simulation model was validated using an engine equipped with a high pressure electronic unit injector (HEUI) system. Three important parameters were used for the optimization - boost pressure, EGR rate and start of injection timing. Numerical optimization identified HCCI-like combustion characteristics showing significant improvements for the soot and $NO_X$ emissions. The optimized soot and $NO_X$ emissions were reduced to 0.005 g/kW-hr and 1.33 g/kW-hr, respectively. Moreover, the optimum results met EPA 2007 mandates at the operating point considered.

• Korean Journal of Cognitive Science
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• v.4 no.2
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• pp.181-200
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• 1994

In the learning phase of multilyer feedforword neural network,there are problems such that local minimum,learning praralysis and slow learning speed when backpropagation algorithm used.To overcome these problems, the genetic algorithm has been used as learing method in the multilayer feedforword neural network instead of backpropagation algorithm.However,because the genetic algorith, does not have any mechanism for fine-tuned local search used in backpropagation method,it takes more time that the genetic algorithm converges to a global optimal solution.In this paper,we suggest a new GA-BP method which provides a fine-tunes local search to the genetic algorithm.GA-BP method uses gradient descent method as one of genetic algorithm's operators such as mutation or crossover.To show the effciency of the developed method,we applied it to the 3-parity bit problem with analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.79-87
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• 2017

The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro- genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.138-145
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• 2000

A identification algorithm that finds optimal fuzzy membership functions and rule base to fuzzy model isproposed and a fuzzy controller is designed to get more accurate position and velocity control of wheeled mobile robot. This procedure that is composed of three steps has its own unique process at each step. The elements of output term set are increased at first step and then the rule base is varied according to increase of the elements. The adjusted system is in competition with system which doesn't include any increased elements. The adjusted system will be removed if the system lost. Otherwise, the control system is replaced with the adjusted system. After finished regulation of output term set and rule base, searching for input membership functions is processed with constraints and fine tuning of output membership functions is done.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.95-103
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• 1995

Recently the trends toward reduction in size of industrial products have increased the application of micro drilling. But micro drilling has still much difficulty so that the needs for active control which give adaptation to controller are expanding. In this paper initial cutting condition was determined for some sorkpieces by experiment and GA-based Fuzzy controller was devised by genetic algorithms and fuzzy inference. The fuzzy inference has been applied to the various prob- lems. However the determination of the membership function is one of the difficult problem. So we introduce a genetic algorithms and propose a self-tuning method of fuzzy membership function. Based on this intelligent control, automation of micro drilling was carried out like the cutting process of skilled machinist.