• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.590-595
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• 2003

In this paper, we propose a new method to the speed control of soccer robot using messy Genetic Algorithm(mGA). In order to arrive in the target of the soccer robot within the smallest time ,we propose the speed control function with several parameters which represent the reflection ratio distance and angle error. Also, we propose the algorithm for searching these parameters by using messy Genetic Algorithm. As a result of finding the optimal parameters, we can move the robot the most quickly in the target under the complex environment.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.2
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• pp.145-154
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• 2004

A long-term sewer rehabilitation project consuming an enormous budget needs to be conducted systematically using an optimization skill. The optimal budgeting and ordering of priority for sewer rehabilitation projects are very important with respect to the effectiveness of investment. In this study, the sewer rehabilitation optimization model using fast-messy genetic algorithm is developed to suggest a schedule for optimal sewer rehabilitation in a subcatchment area by modifying the existing GOOSER$^{(R)}$ model having been developed using simple genetic algorithm. The sewer rehabilitation optimization model using fast-messy genetic algorithm can improve the speed converging to the optimal solution relative to GOOSER$^{(R)}$, suggesting that it is more advantageous to the sewer rehabilitation in a larger-scale subcatchment area than GOOSER.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.157-160
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• 2000

This paper deals with the fuzzy modeling for the complex and uncertain system in which conventional and mathematical models may fail to give satisfactory results. mGA(messy Genetic Algorithm) has more effective and adaptive structure than sGA with respect to using changeable-length string and VEGA(Virus Evolution Genetic) Algorithm) can search the global and local optimal solution simultaneously with reverse transcription operator and transduction operator. Therefore in this paper, the optimal fuzzy model is obtained using Virus-messy Genetic Algorithm(Virus-mGA). In this method local information is exchanged in population so that population may sustain genetic divergence. To prove the surperioty of the proposed approach, we provide the numerical example.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.53-58
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• 2002

In the construction of successful fuzzy models for nonlinear systems, the identification of an optimal fuzzy model system is an important and difficult problem. Traditionally, sGA(simple genetic algorithm) has been used to identify structures and parameters of fuzzy model because it has the ability to search the optimal solution somewhat globally. But SGA optimization process may be the reason of the premature local convergence when the appearance of the superior individual at the population evolution. Therefore, in this paper we propose a new method that can yield a successful fuzzy model using VmGA(virus messy genetic algorithms). The proposed method not only can be the countermeasure of premature convergence through the local information changed in population, but also has more effective and adaptive structure with respect to using changeable length string. In order to demonstrate the superiority and generality of the fuzzy modeling using VmGA, we finally applied the proposed fuzzy modeling methodof a complex nonlinear system.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.451-456
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• 2005

In this paper, we propose the navigation control method for intelligent robot using messy genetic algorithm. The fuzzy controller design for navigation of the intelligent robot was dependant on expert's knowledge. But, the parameters of the fuzzy logic controller obtained from expert's control action may not be outimal. In this paper, to solve the above problem, we propose the identification method to automatically tune the number of fuzzy rule and parameters of memberships of fuzzy controller using mGA. Finally, to show and evaluate the generality and feasibility of the proposed method, we provides some simulations for wall following navigation of intelligent robot.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2233-2235
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• 2003

본 논문에서는 mGA를 이용해 축구로봇의 속도를 제어하는 새로운 방법을 제안하였다. 축구 로봇의 목표를 최소 시간내에 도착하기 위해 속도제어에 크게 영향을 미치는 거리 오차와 각도 오차 등의 비율를 나타내는 각종 파라미터가 포함되어 있는 제어 함수를 제시하고, 이들 파라미터들을 mGA을 이용하여 최적의 값들을 탐색함으로써 변화되는 환경 속에서도 로봇의 목적지에 최소 시간내에 이동하도록 속도제어 전략을 제안한다.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1952-1954
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• 2001

In this paper, we propose the neuro-fuzzy modeling method using VmGA (Virus messy Genetic Algorithm) for the complex nonlinear system. VmGA has more effective and adaptive structure than sGA. in this paper, we suggest a new coding method for applying the model's input and output data to the optimal number of rules in fuzzy models and the structure and parameter identification of membership functions simultaneously. The proposed method realizes the optimal fuzzy inference system using the learning ability of neural network. For fine-tune of parameters identified by VmGA, back- propagation algorithm is used for optimizing the parameter of fuzzy set. The proposed fuzzy modeling method is applied to a nonlinear system to prove the superiority of the proposed approach through comparing with ANFIS.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.507-509
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• 1999

In this paper, we propose a new fuzzy model identification method that can yield a successful fuzzy rule base for fundamental approximations. The method in this paper uses a set of input-output data and is based on a hybrid messy genetic algorithm (mGA) with a fine-tuning scheme. The mGA processes variable-length strings, while standard GAs work with a fixed-length coding scheme. For successfully identifying a complex nonlinear system, we first use the mGA, which coarsely optimizes the structure and the parameters of the fuzzy inference system, and then the gradient descent method which tine tunes the identified fuzzy model. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a nonlinear approximation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.423-431
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• 2000

This paper presents a systematic approach to the input-output data-based fuzzy modeling for the complex and uncertain nonlinear systems, in which the conventional mathematical models may fail to give the satisfying results. To do this, we propose a new method that can yield a successful fuzzy model using a mGA hybrid schemes with a fine-tuning method. We also propose a new coding method fo chromosome for applying the mGA to the structure and parameter identifications of fuzzy model simultaneously. During mGA search, multi-purpose fitness function with a penalty process is proposed and adapted to guarantee the accurate and valid fuzzy modes. This coding scheme can effectively represent the zero-order Takagi-Sugeno fuzzy model. The proposed mGA hybrid schemes can coarsely optimize the structure and the parameters of the fuzzy inference system, and then fine tune the identified fuzzy model by using the gradient descent method. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its applications to two nonlinear systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.187-190
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• 2002

주어진 시스템의 정확한 제어를 위해 뉴로-퍼지 제어시스템의 성공적인 제어는 그 네트웍의 구성에 크게 의존한다. 현재 유전알고리즘을 사용한 제어기 구조의 최적화 방법에 대한 많은 연구가 이루어지고 있으나, 기존의 유전 알고리즘은 고정된 길이의 스트링 구조로 인하여 적합한 연계(linkage)를 얻기 어렵다는 단점이 있다 본 논문에서는 뉴로-퍼지 제어기의 구조적 최적화 설계의 새로운 방법을 제안한다. 여기서, 우리는 구조적으로 최적화 된 뉴로-퍼지 제어기를 설계하기 위해 가변길이 스트링을 사용하는 메시 유전 알고리즘(messy Genetic Algorithm mGA)을 사용한다. 그리고 제안된 방법의 우수성을 증명하기 위해 대표적인 비선형 시스템인 cart-pole 시스템에 제안된 방법을 적용한다.