• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.69-74
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• 2004

A sequencing problem is to find an ordered sequence of some entities which maximizes (or minimize) the domain specific objective function. As some typical examples of sequencing problems, there are traveling salesman problem, job shop scheduling, flow shop scheduling, and so on. This paper introduces a new type of sequencing problems, named a sequencing problem with fuzzy preference relation, where a fuzzy preference relation is provided for the evaluation of the quality of sequences. It presents how such a problem can be formulated in terms of objective function. It also proposes a genetic algorithm applicable to such a sequencing problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.781-786
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• 2008

In this paper, we propose a novel approach predicting the fluctuation of stock index by finding a relation in various stock indexes that are represented by linear combinations. The important points are to select stock indexes related to predicting indexes and to find the proper relations in them. Since it is unattainable to use entire stock indexes relation, we used only data that are closely associated with each other. We used Genetic Algorithm(GA) to find the most suitable stock-index relation. We simulated the investment in years from 2005 to 2007 with each real index. Finally we verified that the investment money increased 230 percents by the proposed method.

• The Journal of Pediatrics of Korean Medicine
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• v.24 no.3
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• pp.138-149
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• 2010

Objectives: The purpose of this study is to estimate genetic and environmental factors which can effect growth, and predict final height using this factors. Methods: Correlation analysis and regression analysis were conducted between measurements of height and Genetic & environmental factors through survey from 1352 child & adolescent patients. Results: Factors which have correlation with height percentile are MPH(Mid-Parental Height), NBW(Neonatal body weight), anorexia, dyspepsia, atopic dermatitis, frequency of breakfast and quality of sleeping time. MPH has moderate relation, NBW and anorexia have fair relation, and other factors have linear but poor relation. Regression equation from factors which have correlation and height percentile has 26.9% of predictive power. Regression equation considering only genetic factor has 20.4%. MPH has the most effect on height percentile. Anorexia has more effect than NBW. Other factors also have small and similar effect. Conclusions: Height of parents has the most effect on growth, anorexia, dyspepsia, atopic dermatitis, frequency of breakfast and quality of sleeping time also has effect.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.91-95
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• 2003

Yacht design is significantly affected by the hull geometrical characteristics. Therefore, it is necessary to closely examine the relation between hull and performance, before considering characteristics of sea condition. In this study, Genetic Programming is used to derive a formula the relationship between hull geometric characteristics and performance. Using the formula, a new guideline is proposed to determine performance of a yacht.

• Korean Journal of Plant Resources
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• v.15 no.3
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• pp.250-259
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• 2002

Agromonia pilosa Ledeb. has been used as a medicinal plant in traditional folk remedy. There are few reports on classification, physiology, ecology and morphological studies of Agromonia pilosa L. in Korea. Therefore, advanced approaches on study and development with this plant would be done urgently. Present stndy was carried out to gain basic information on genetic resources and variation with collected domestic landraces through RAPD analysis in Agromonia pilosa L. Forty two collections of Agromonia pilosa L. from nation-wide area including USA one were analyzed by RAPD test. Molecular marker size by amplified DNA band pattern ranged from 300 to 2,100bp. Among the collection, two landraces of Hadong and Cheonghak-dong showed close relation in genetic similarity. Minimum and maximum value by matrix of 1-F among 26 collected landraces were figured out as 0.365 and 0.827 showing mean value for 0.624, respectively. Those landraces were classified into two groups with cluster analysis by Nei and Li's formula from RAPD-analyzed values, and considerable genetic differences were recognized between two groups.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.832-839
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• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.354-360
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• 1998

In general, it is very difficult to find optimal fuzzy rules by experience when a system is dynamical and/or complex. Futhermore proper fuzzy partitioning is not deterministic and there is no unique solution. Therefore we propose a new design method of an optimal fuzzy logic controller, that is a co-evolutionary genetic algorithm finding optimal fuzzy rule and proper membership functions at the same time. We formalize the relation between fuzzy rules and membership functions in terms of fitness. We review the typical approaching methods to co-evolutionary genetic algorithms , and then classify them by fitness relation matrix. Applications of the proposed method to a path planning problem of autonomous mobile robots when moving objects exist are presented to demonstrate the performance and effectiveness of the method.

• Structural Monitoring and Maintenance
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• v.5 no.4
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• pp.445-461
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• 2018

The aim of this research is to evaluate the effects of the number of spans, height of spans, number of floors, height of floors, column to beam moment of inertia ratio, and plastic joints distance of beams from columns on the ductility of moment frames. For the facility in controlling the ductility of the frames, this paper offers a simple relation instead of complex equations of different codes. For this purpose, 500 analyzed and designed frames were randomly selected, and their ductility was calculated by the use of nonlinear static analysis. The results cleared that the column-to-beam moment of inertia ratio had the highest effect on ductility, and if this relation was more than 2.8, there would be no need for using the complex relations of codes for controlling the ductility of frames. Finally, the ductility of the most frames of this research could be estimated by using the combination of genetic algorithm and artificial neural networks properly.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.640-645
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• 1998

A Sequencing problem is one to find an ordered sequence of some entities which maximizes (or minimize) some objective function. This paper introduces an new type of sequencing problems, named a Sequencing problem with fuzzy preference relation is previded for the evaluation of the quality of sequences, It presents how such a problem can be formulated in the point of objective function. In addition, it proposes a genetic algorithm applicable to such a sequencing problem.