• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.05a
• /
• pp.993-996
• /
• 2004

사람들 사이에는 DNA 서열의 변이로 인한 유전적 차이가 있으며, 가끔 이러한 차이가 유전 질병의 원인이 되기도 한다. 일반적으로 DNA에서 가장 잘 알려진 변이가 바로 SNP(Single Nucleotide Polymorphism : 스닙)이다. SNP는 보통 블록단위로 유전되어지며 한쪽 부모로부터 유전되어진 SNP 블록을 SNP 하플로타입이라고 부른다. 생물학 실험을 통하여 추출되어진 결과물은 부모로부터 유전되어진 대립 유전자가 혼합되어진 지노타입(genotype)의 정보이다. 지노타입은 직관적으로 정확한 SNP 하플로타입을 추정하기가 힘들고, 생물학 실험을 통하여 하플로타입(haplotype)을 분석하는데 많은 비용이 들기때문에, 이를 컴퓨터 계산을 통하여 추론하는 연구가 Clark[1]에 의해서 제안되어진 이후 활발하게 진행되고 있다. 본 논문에서는 하플로타입을 효과적으로 추론하기 위해 유전자 알고리즘을 이용한 새로운 방법을 설명하고, 실험 결과를 기존의 연구 결과와 비교 분석한다.

• Proceedings of the KSME Conference
• /
• 2003.04a
• /
• pp.1639-1644
• /
• 2003

A hybrid genetic algorithm is developed for estimating the wall emissivities for an absorbing, emitting, and scattering media in a two-dimensional irregular geometry with diffusely emitting and reflecting opaque boundaries by minimizing an objective function, which is expressed by the sum of square errors between estimated and measured temperatures at only four data positions. The finite-volume method was employed to solve the radiative transfer equation for a two-dimensional irregular geometry. The results show that a developed hybrid genetic algorithms reduce the effect of genetic parameters on the performance of genetic algorithm and that the wall emissivities are estimated accurately without measurement errors.

• Journal of Korea Society of Industrial Information Systems
• /
• v.25 no.2
• /
• pp.115-127
• /
• 2020

In this paper, a sustainable close-loop supply chain (SCLSC) model is proposed for effectively managing the production, distribution and handling process of mobile phone. The proposed SCLSC model aims at maximizing total profit as economic factor, minimizing total CO₂ emission amount as environmental factor, and maximizing social influence as social factor in order to reinforce sustainability in it. Since these three factors are represented as each objective function in modeling, the proposed SCLSC model can be taken into consideration as a multi-objective optimization problem and solved using a hybrid genetic algorithm (HGA) approach. In numerical experiment, three different scales of the SCLSC model are presented and the efficiency of the HGA approach is proved using various measures of performance.

• Journal of Korea Society of Industrial Information Systems
• /
• v.26 no.3
• /
• pp.9-22
• /
• 2021

This study proposes a supply chain network (SCN) model considering supply disruption in assembly industry. For supply disruption, supplier disruption and its route disruption are simultaneously taken into consideration in the SCN model. With the simultaneous consideration, the SCN model can achieve its flexibility and efficiency. A mathematical formulation is suggested for representing the SCN model, and a proposed hybrid genetic algorithm (pro-HGA) is used for implementing the mathematical formulation. In numerical experiment, the performance of the pro-HGA approach is compared with those of some conventional approaches using the SCN models with various scales, and a sensitivity analysis considering the change of the numbers of suppliers and backup routes is done. Experimental results show that the performances of the pro-HGA approach are superior to those of the conventional approaches, and the flexibility and efficiency of the SCN model considering supply disruption are proved. Finally, the significance of this study is summarized and a potential future research direction is mentioned in conclusion.

• Journal of Korea Society of Industrial Information Systems
• /
• v.26 no.1
• /
• pp.55-65
• /
• 2021

In this paper, a methodology for reinforcing reverse logistics (RL) activities in a closed-loop supply chain (CLSC) model is proposed. For the methodology, the activities of the recovery center (RC) which can be considered as one of the facilities in the RL are reinforced. By the reinforced activities in the RC, the recovered parts and products after checking and recovering processes of the returned product from customer can be reused in the forward logistics (FL) of the CLSC model. A mathematical formulation is suggested for representing the CLSC model with reinforced RL activities, and implemented using a hybrid genetic algorithm (HGA) approach. In numerical experiment, two different scales of the CLSC model are presented and the performance of the HGA approach is compared with those of some conventional approaches. The experimental results show that the former outperforms the latter in most of performance measures. The robustness of the CLSC model is also proved by regulating various rates of the recovered parts and products in the RC.

• Journal of Korea Society of Industrial Information Systems
• /
• v.20 no.6
• /
• pp.57-65
• /
• 2015

Manufacturing optimization problem is to find the optimal solution under satisfying various and complicated constraints with the design variables of nonlinear types. To achieve the objective, this paper proposes a hybrid approach. The proposed hybrid approach is consist of genetic algorithm(GA), cuckoo search(CS) and hill climbing method(HCM). First, the GA is used for global search. Secondly, the CS is adapted to overcome the weakness of GA search. Lastly, the HCM is applied to search precisely the convergence space after the GA and CS search. In experimental comparison, various types of manufacturing optimization problems are used for comparing the efficiency between the proposed hybrid approach and other conventional competing approaches using various measures of performance. The experimental result shows that the proposed hybrid approach outperforms the other conventional competing approaches.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.14 no.8
• /
• pp.803-808
• /
• 2003

Several dielectric pipes buried in the lossy half space are reconstructed from the scattered fields measured along the interface between the air and the lossy ground. Iterative inversion method by using the hybrid optimization algorithm combining the genetic and the Levenberg-Marquardt algorithm enables us to find the positions, the sizes, and the medium parameters such as the permittivities and the conductivities of the buried pipes as well as those of the background lossy half space even when the dielectric pipes are close together. Illposedness of the inversion caused by the errors in the measured scattered fields are regularized by filtering the evanescent modes of the scattered fields out.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.12
• /
• pp.49-56
• /
• 2023

In this study, we propose a genetic algorithm (GA) to optimize the allocation and operation order of transporters. The solution in the GA is represented by a set of lists each of which the operation order of the corresponding transporter. In addition, it was implemented in the form of a hybrid genetic algorithm combining effective local search operations for performance improvement. The local search reduces the number of operating transporters by moving blocks from a transporter with a low workload into that with a high workload. To evaluate the effectiveness of the proposed algorithm, it was compared with Multi-Start and a pure genetic algorithm through a simulation environment similar in scale to an actual shipyard. For the largest problem, compared to them, the number of transporters was reduced by 40% and 34%, and the total task time was reduced by 27% and 17%, respectively.

• Journal of KIISE:Software and Applications
• /
• v.34 no.9
• /
• pp.857-868
• /
• 2007

The good classifier ensemble should have a high complementarity among classifiers in order to produce a high recognition rate and its size is small in order to be efficient. This paper proposes a classifier ensemble selection algorithm with coarse-to-fine stages. for the algorithm to be successful, the original classifier pool should be sufficiently diverse. This paper produces a large classifier pool by combining several different classification algorithms and lots of feature subsets. The aim of the coarse selection is to reduce the size of classifier pool with little sacrifice of recognition performance. The fine selection finds near-optimal ensemble using genetic algorithms. A hybrid genetic algorithm with improved searching capability is also proposed. The experimentation uses the worldwide handwritten numeral databases. The results showed that the proposed algorithm is superior to the conventional ones.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.615_616
• /
• 2009

유한요소해석(Finite Element Analysis)을 통한 풍력발전기 최적설계시, 해석 특성상 발생하는 막대한 소요시간의 개선이 필요하다. 본 논문에서는 연간 에너지 생산량(Annual Energy Production : AEP)의 최대화를 목표로 GA(Genetic Algorithm)와 MADS(Mesh Adaptive Direct Search)를 결합한 혼합 알고리즘을 이용하여 최적설계를 수행하였다. 또한, 혼합 알고리즘과 병렬분산 유전알고리즘을 이용한 최적설계의 해석 소요시간을 비교 및 검토하였다.