• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.149-156
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• 2010

Since the premature convergence phenomenon of genetic algorithms (GAs) degrades the performances of GAs significantly, solving this problem provides a lot of effects to the performances of GAs. In this paper, we propose a selective mutation method in order to improve the performances of GAs by alleviating this phenomenon. In the selective mutation, individuals are additionally mutated at the specific region according to their ranks. From this selective mutation, individuals with low ranks are changed a lot and those with high ranks are changed small in the phenotype. Finally, some good individuals search around them in detail and the other individuals have more chances to search new areas. This results in enhancing the performances of GAs through alleviating of the premature convergence phenomenon. We measured the performances of our method with four typical function optimization problems. It was found from experiments that our proposed method considerably improved the performances of GAs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.305-308
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• 2005

본 논문은 유전자 알고리즘 기반 퍼지 다항식 뉴럴네트워크(Genetic Algorithm-based Fuzzy Polynomial Neural Networks ; GAs-based FPNN)를 이용하여 비선형 데이터의 최적화 추론 알고리즘을 제안한다. FPNN의 각 노드는 GMDH와 퍼지규칙을 기초로 만들었다. FPNN의 각 노드는 퍼지 다항식 뉴론(Fuzzy Polynomial Neuron : FPN)이라고 표현하다. 제안된 모델은 구조 선택에 있어서 유전자 알고리즘(Genetic Algorithms : GAs)을 이용하였다. 유전자 알고리즘을 사용하여 입력의 차수와 입력의 개수 그리고 후반부 추론의 형태를 최적 선택하였다. 비선형 데이터에 대한 모델 설계를 위해 최적화 알고리즘인 유전자 알고리즘 기반 FPNN 모델 설계가 유용하고 효과적임을 보인다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.299-303
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• 2005

In the present study, in order to improve precision of the component characteristic maps generated by the scaling method, a map generation method which can produce a compressor map from some experimental performance data using GAs(Genetic Algorithms) was proposed. However, in case of the proposed map generation method only using GAs, because it has a drawback for estimating correctly the surge points and the choke points of the compressor map, a modified GAs method was additionally proposed through complementally use of the scaling method to determine obviously those points of the compressor map.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.3
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• pp.238-247
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• 2007

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to learning algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generator turbine and power turbine are considered for engine performance deterioration and estimation for performance deterioration of a single component at design point was conducted. As a result of that, defect diagnostics has been conducted. The input criteria for the genetic algorithm to guarantee the high stability and reliability was discussed as increasing learning data sets. As a result, the accuracy of defect estimation and diagnostics were verified with its RMS error within 3%.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.285-288
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• 2006

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to teaming algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generation turbine and power turbine are considered for estimation for performance deterioration of a complex component at design point was conducted. As a result of that, complex defect diagnostics has been conducted. As a result, the accuracy of diagnostics were verified with its relative error with in 10% at each component.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.2
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• pp.71-83
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.366-369
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• 2006

본 논문에서는 계층적 공정 경쟁 유전자 알고리즘을 통한 비선형시스템의 정보입자 기반 퍼지집합 퍼지집합 모델의 최적화 방법을 제안한다. 퍼지집합 모델은 주로 전문가의 경험에 기반을 두어 얻어지기 때문에 동정과 최적화 과정이 필요하며 GAs를 이용하여 퍼지모델을 최적화한 연구가 많이 있다. GAs는 전역 해를 찾을 수 있는 최적화 알고리즘으로 잘 알려져 있지만 조기 수렴 문제를 포함하고 있다. 병렬유전자 알고리즘(PGA)은 조기수렴를 더디게 하고 전역 해를 찾기 위한 진화알고리즘이다. 적응형 계층적 공정 경쟁기반 유전자 알고리즘(AHFCGA)을 이용하여 퍼지모델의 입력변수, 멤버쉽함수의 수, 멤버쉽함수의 정점 등의 전반부 구조와 파라미터를 동정하였고, LSE를 사용하여 후반부 파라미터를 동정하였으며 실험적 예제를 통하여 제안된 방법의 성능을 평가한다.

• Journal of the Korean Society of Propulsion Engineers
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• v.8 no.3
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• pp.44-52
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• 2004

In this study, a component map generation method using experimental data and the genetic algorithms are newly proposed. In order to generate the performance map for components of this engine, after obtaining engine performance data through many experimental tests, and then the third order equations which have relationships the mass flow function the pressure ratio and the isentropic efficiency as to the engine rotational speed were derived by using the genetic algorithms. A steady-state performance analysis was performed with the generated maps of the compressor by the commercial gas turbine performance analysis program GASTURB. In comparison, it was found that the component maps can be generated from the experimental test data by using the genetic algorithms, and it was confirmed that the analysis results using the generated maps were very similar to those using the scaled maps from the GASTURB.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2004.10a
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• pp.195-200
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• 2004

In this study, a component map generation method using experimental data and the genetic algorithms are newly proposed. In order to generate the performance map for components of this engine, after obtaining engine performance data through many experimental tests, and then the third order equations which have relationships the mass flow function the pressure ratio and the isentropic efficiency as to the engine rotational speed were derived by using the genetic algorithms. A steady-state performance analysis was peformed with the generated maps of the compressor by the commercial gas turbine performance analysis program GASTURB(1). In comparison, it was found that the component maps can be generated from the experimental test data by using the genetic algorithms, and it was confirmed that the analysis results using the generated maps were very similar to those using the scaled maps from the GASTURB.

• Journal of Ocean Engineering and Technology
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• v.16 no.4
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• pp.25-31
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• 2002

This paper is to find optimum design of plane framed structures with discrete variables. Global search algorithms for this problem are Genetic Algorithms(GAs), Simulated Annealing(SA) and Shuffled Complex Evolution(SCE), and hybrid methods (GAs-SA, GAs-SCE). GAs and SA are heuristic search algorithms and effective tools which is finding global solution for discrete optimization. In particular, GAs is known as the search method to find global optimum or near global optimum. In this paper, reinforced concrete plane frames with rectangular section and steel plane frames with W-sections are used for the design of discrete optimization. These structures are designed for stress constraints. The robust and effectiveness of Genetic Algorithms are demonstrated through several examples.