• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1349-1350
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• 2015

풍속은 다른 기상요소들보다 순간 변동이 심하고 국지성이 강하여 수치 예보 모델만으로 예측의 정확성을 높이기가 어렵다. 기상청의 단기 풍속 예보는 전 지구적 통합 예보모델인 UM(Unified Model)의 예측값에 MOS(Model Output Statictics)를 통한 보정을 수행하며, 보정식의 생성에 다중선형회귀분석 방법을 사용한다. 본 연구자는 유전프로그래밍(Genetic Programming)을 이용한 비선형 회귀분석 기반의 보정식 생성을 통하여 이를 개선한 바 있는데, 본 연구에서는 보다 향상된 성능을 얻기 위하여 GP 기법 측면에서 Automatically Defined Functions과 다군집(Multiple Populations) 수행을 통해 성능을 높이고자 한다.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.99-104
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• 2002

This paper addresses a problem of machining route selection and determination of input quantity with yield in multi-stage flexible flow system. The problem is formulated as nonlinear programing and the proposed model is solved by genetic algorithm(GA) approach. The effectiveness of the proposed GA approach is evaluated through comparisons with the optimal solution obtained from the branch and bound for the same problem.

• Journal of the Korean Society of Safety
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• v.13 no.4
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• pp.240-247
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• 1998

This paper proposed a method for solving the nonlinear integer programing problem to get easily the best compromise solution while holding a nonlinear property by using the genetic algorithms. Also, this paper reported that the optimization problem of systems reliability as was solved by using the preposed method, and the numerical comparison experiments between the 0-1 LP/0-1 NP formulations were demonstrated, and from the quantitative evaluation the efficiency of the proposed method was demonstrated.

• Korean Management Science Review
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• v.23 no.1
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• pp.63-74
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• 2006

Decision tree induction algorithm is one of the most widely used methods in classification problems. However, they could be trapped into a local minimum and have no reasonable means to escape from it if tree algorithm uses top-down search algorithm. Further, if irrelevant or redundant features are included in the data set, tree algorithms produces trees that are less accurate than those from the data set with only relevant features. We propose a hybrid algorithm to generate decision tree that uses genetic programming with sequentially selected features. Correlation-based Feature Selection (CFS) method is adopted to find relevant features which are fed to genetic programming sequentially to find optimal trees at each iteration. The new proposed algorithm produce simpler and more understandable decision trees as compared with other decision trees and it is also effective in producing similar or better trees with relatively smaller set of features in the view of cross-validation accuracy.

• Journal of Information Technology Application
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• v.3 no.3
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• pp.25-40
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• 2001

This paper deals with the problem of generating optimal polynomials using Genetic Programming(GP). The polynomial should approximate nonlinear response surfaces. Also, there should be a consideration regarding the size of the polynomial, It is not desirable if the polynomial is too large. To build small or medium size of polynomials that enable to model nonlinear response surfaces, we use the low order Tailor series in the function set of GP, and put the constrain on generating GP tree during the evolving process in order to prevent GP trees from becoming too large size of polynomials. Also, GAGPT(Group of Additive Genetic Programming Trees) is adopted to help achieving such purpose. Two examples are given to demonstrate our method.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.46-51
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• 1997

The optimal control of a continuous process has been performed using genetic algorithms(GAs). GAs are robust and easily applicable for complex and highly nonlinear problems. We introduce the heuristics 'dynamic range' which reduces the search space dramaticaly keeping the robust search of GAs. GAs with dynamic range show the better performance than SQP(Successive Quadratic Programing) method which converges to a local minimum. The proposed methology has been applied to the optimal control of the continuous MMA-VA copolymerization reactor for the production of the desired molecular wieght and the composition of VA in dead copolymer.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.1
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• pp.64-73
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• 2004

This paper addresses a problem of machining determination of input quantity in a multi-stage flexible flow system with non-identical parallel machines considers a subcontracting, machining restraint, and machine yield. We develop a nonlinear programing with the objective of minimizing the sum of in-house processing cost and subcontracting cost. To solve this model, we introduce a single-processor parallel genetic algorithm(SPGA) to improve a weak point for the declined robustness of simple algorithm(SGA). The efficiency of the SPGA is examined in comparison with the SGA for the same problem. In of examination the SPGA is to provide the excellent solution than the solution of the SGA.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.121-123
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• 2009

뱀형 모듈라 로봇은 다양한 환경에 대해서 강인성을 가지고 있고, 모듈 일부의 고장에도 이동할 수 있는 장점을 가진다. 그러나 이동 제어 방법이 어렵고, 아직까지 효율적인 이동법의 개발이 미비한 편이다. 본 연구에서는 뱀형 로봇의 이동제어를 위하여 GA(Genetic Algorithm)기반의 위상제어 방식과 GP(Genetic Programing)를 사용한 임의의 관절궤적 생성 방식을 비교한다. KMC사의 뱀형 로봇을 대상으로 먼저 webots 시뮬레이터 상에서 모델링 및 시뮬레이션을 수행하여 위의 방법들을 비교하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.236-240
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• 2000

A cellular automaton is well-known for self-organizing and dynamic behaviors in the field of artificial life. This paper addresses a new neuronic architecture called an evolvable cellular classifier which evolves with the genetic rules (chromosomes) in the non-uniform cellular automata. An evolvable cellular classifier is primarily based on cellular programing, but its mechanism is simpler because it utilizes only mutations for the main genetic operators and resembles the Hopfield network. Therefore, the desirable hi t-patterns could be obtained through evolutionary processes for just one individual agent. As a result, an evolvable hardware is derived which is applicable to classification of bit-string information.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.54
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• pp.13-26
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• 2000

Machining route selection to produce parts should be based on shop flow information because of input data at scheduling tasks and is one of the main problem in process planning. This paper addresses the problem of machining route selection in multi-stage process with machine group included a similar function. The model proposed is formulated as 0-1 integer programing considering the relation of parts and machine table size, avaliable time of each machine for planning period, and delivery date. The objective of the model is to minimize the sum of processing, transportation, and setup time for all parts. Genetic algorithm approach is developed to solve this model. The efficiency of the approach is examined in comparison with the method of branch and bound technique for the same problem. Also, this paper is to solve large problem scale and provide it if the multiple machining routes are existed an optimal solution.