• 경영과학
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• 제13권1호
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• pp.97-109
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• 1996

Genetic Algorithms have been successfully applied to various problems (for example, engineering design problems with a mix of continuous, integer and discrete design variables) that could not have been readily solved with traditional computational techniques. But, several problems for which conventional Genetic Algorithms are ill defined are premature convergence of solution and application of exterior penalty function. Therefore, we developed an Improved Genetic Algorithms (IGAs) to solve above two problems. As a case study, IGAs is applied to several nonlinear optimization problems and it is proved that this algorithm is very useful and efficient in comparison with traditional methods and conventional Genetic Algorithm.

• 경영과학
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• 제12권2호
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• pp.105-127
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• 1995

This paper solves different machine-part group formation (MPGF) problems using genetic algorithms to demonstrate that it can be a new robust alternative to the conventional heuristic approaches for optimization problems. We first give an overview of genetic algorithms: Its principle, various considerations required for its implementation, and the method for setting up parameter values are explained. Then, we describe the MPGF problem which are critical to the successful operation of cellular manufacturing or flexible manufacturing systems. We concentrate on three models of the MPGF problems whose forms of the objective function and/or constraints are quite different from each other. Finally, numerical examples of each of the models descibed above are solved by using genetic algorithms. The result shows that the solutions derived by genetic algorithms are comparable to those obtained through problem-specific heuristic methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권1호
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• pp.29-35
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• 2012

This paper introduces an adaptive control method of strong mutation rate and probability for queen-bee genetic algorithms. Although the queen-bee genetic algorithms have shown good performances, it had a critical problem that the strong mutation rate and probability should be selected by a trial and error method empirically. In order to solve this problem, we employed the measure of convergence and used it as a control parameter of those. Experimental results with four function optimization problems showed that our method was similar to or sometimes superior to the best result of empirical selections. This indicates that our method is very useful to practical optimization problems because it does not need time consuming trials.

• 한국전자파학회:학술대회논문집
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• 한국전자파학회 2003년도 종합학술발표회 논문집 Vol.13 No.1
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• pp.428-431
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• 2003

In this paper, distances between elements are optimized for low sidelobe level (SLL) microstrip patch array using Genetic Algorithms. Genetic Algorithms are "global" numerical-optimization methods, it's advantages are very simple coding and fast optimization. This paper show how to optimize the maximum SLL using Genetic Algorithms. In the results, although mutual coupling is neglected, it's maximum SLL is 3.5 dB lower than Uniformly Spaced Array(distance=$0.5{\lambda}$).

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.270-272
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• 2006

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 춘계학술대회논문집A
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• pp.725-730
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• 2000

Genetic algorithms based on the theory of natural selection, have been applied to many different fields, and have proven to be relatively robust means to search for global optimum and handle discontinuous or even discrete data. Genetic algorithms are widely used for unconstrained optimization problems. However, their application to constrained optimization problems remains unsettled. The most prevalent technique for coping with infeasible solutions is to penalize a population member for constraint violation. But, the weighting of a penalty for a particular problem constraint is usually determined in the heuristic way. Therefore this paper proposes, the effective technique for handling constraints, the ranking penalty method and hybrid genetic algorithms. And this paper proposes dynamic mutation tate to maintain the diversity in population. The effectiveness of the proposed algorithm is tested on several test problems and results are discussed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.275-278
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• 1996

The application of genetic algorithms to fuzzy rule generation holds a great deal of promise in overcoming difficult problems in fuzzy systems design. There are some aspects to be considered when genetic algorithms are used for generating fuzzy rules. In this paper, we will present an aspect about the control surface constructed by the resultant rules. In the extensive simulations, an important observation that the rules searched by genetic algorithms are randomly scattered is made and a solution to this problem is provided by including a smoothness cost in the objective function. We apply the fuzzy rules generated by genetic algorithms to the fuzzy truck backer-upper control system and compare them with the rules made by an expert.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1021-1023
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• 1996

This paper proposes a design of self-tuning fuzzy rules and membership functions based on genetic algorithms. Sub-optimal fuzzy rules and membership functions are found by using genetic algorithms. Genetic algorithms are used for tuning fuzzy rules and membership functions. A arbitrary speed trajectories are selected for the reference input of the proposed methods. Experimental results show the good performance in the DC motor control system with the self-tuning fuzzy controller based on genetic algorithms.

• 산업경영시스템학회지
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• 제22권49호
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• pp.115-123
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• 1999

An optimization problem is to select the best of many possible design alternatives in a complex design space. Genetic algorithms, one of the numerous techniques to search optimal solution, have been successfully applied to various problems (for example, parameter tuning in expert systems, structural systems with a mix of continuous, integer and discrete design variables) that could not have been readily solved with more conventional computational technique. But, conventional genetic algorithms are ill defined for two classes of problems, ie., penalty function and fitness scaling. Therefore, this paper develops Improved genetic algorithms(IGA) to solve these problems. As a case study, numerical examples are demonstrated to show the effectiveness of the Improved genetic algorithms.

• 한국지능시스템학회논문지
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• 제13권4호
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• pp.439-447
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• 2003

본 논문에서는 유전자 알고리즘의 성능향상을 위한 비례-적분-미분 평가방법을 제안한다. 비례-적분-미분 평가방법에서는 평가함수에 의하여 계산된 적합도와 더불어 각 개체의 부모 적합도, 초기세대로부터 이전세대까지의 최소, 최대 적합도를 이용하여 평가함으로서 유전자 알고리즘의 성능저하를 가져오는 조숙수렴 (premature convergence) 확률을 줄여주어 결과적으로 유전자 알고리즘의 성능을 향상시키게 된다. 비례-적분-미분 평가방법의 성능을 보이기 위하여 유전자 알고리즘 성능 검증에 많이 사용되어온 대표적인 함수 최적화 문제들을 적용하여 실험해본 결과 제안한 방법이 유전자 알고리즘의 성능을 크게 향상 시킬 수 있음을 확인하였다. 제안한 평가방법은 다른 형태의 유전자 알고리즘의 성능향상을 위해서도 쉽게 적용될수 있다.