• 제어로봇시스템학회논문지
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• 제10권11호
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• pp.1071-1076
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• 2004

It is a fuzzy controller that it is the most used method in the control of non-linear system. The most important part in the fuzzy controller is a design of fuzzy rules. Many algorithm that design fuzzy rules have proposed. And attention to the evolutionary computation is increasing in the recent days. Among them, the co-evolutionary algorithm is used in the design of optimal fuzzy rule. This paper takes advantage of a schema co-evolutionary algorithm. In order to verify the efficiency of the schema co-evolutionary algorithm, a fuzzy controller for the mobile robot control is designed by the schema co-evolutionary algorithm and it is compared with other parasitic co-evolutionary algorithm such as a virus-evolutionary genetic algorithm and a co-evolutionary method of Handa.

• 한국경영과학회지
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• 제34권3호
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• pp.125-136
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• 2009

The container packing problem Is one of the traditional optimization problems, which is very related to the knapsack problem and the bin packing problem. In this paper, we deal with the quadratic multiple container picking problem (QMCPP) and it Is known as a NP-hard problem. Thus, It seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the QMCPP. Until now, only a few researchers have studied on this problem and some evolutionary algorithms have been proposed. This paper introduces a new efficient evolutionary algorithm for the QMCPP. The proposed algorithm is devised by improving the original network random key method, which is employed as an encoding method in evolutionary algorithms. And we also propose local search algorithms and incorporate them with the proposed evolutionary algorithm. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds the new best results in most of the benchmark instances.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.228-233
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• 2004

최근 들어, 다양하고 복잡한 기능을 갖는 로봇이 요구되고 있다. 그러나 이전의 알고리즘으로는 그러한 요구를 만족시킬 수 없다. 이러한 문제를 해결하기 위해, 본 논문에서는 다양한 입력과 출력을 다루는 경우에도 작은 개수의 퍼지 룰을 갖고, 효율적이고 강인하게 제어할 수 있는 2-Layer Fuzzy Controller를 소개한다. 그런데 퍼지 제어기에서의 중요한 문제점은 퍼지 룰 베이스를 어떻게 설계하는지에 달려 있다. 본 논문은 Schema Co-Evolutionary Algorithm을 이용하여 최적의 2-Layer Fuzzy Controller를 설계하는데, 이 Schema Co-Evolutionary Algorithm은 simple GA보다 더 빠르고 우수하게 최적해를 찾을 수 있다.

• 한국국방경영분석학회지
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• 제26권2호
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• pp.120-130
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• 2000

The purpose of this study is to present a method for determining the k most vital arcs in shortest path problem using an evolutionary algorithm. The problem of finding the k most vital arcs in shortest path problem is to find a set of k arcs whose simultaneous removal from the network causes the greatest increase in the total length of shortest path. Generally, the problem determining the k most vital arcs in shortest path problem has known as NP-hard. Therefore, in order to deal with the problem of real world the heuristic algorithm is needed. In this study we propose to the method of finding the k most vital arcs in shortest path problem using an evolutionary algorithm which known as the most efficient algorithm among heuristics. The method presented in this study is developed using the library of the evolutionary algorithm framework and then the performance of algorithm is analyzed through the computer experiment.

• International Journal of Control, Automation, and Systems
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• 제3권3호
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• pp.403-410
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• 2005

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.341-346
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• 2004

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

• 한국지능시스템학회논문지
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• 제11권8호
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• pp.689-694
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• 2001

Recently, several promising multiobjective evolutionary algorithm such as SPEA. NSGA-II, PESA, and SPEA2 have been developed. In this paper, we also propose a new multiobjective evolutionary algorithm that compares to them. In the algorithm proposed in this paper, we introduce a novel concept, “inherited age” and total algorithm is executed based on the inherited age concept. Also, we propose a new sharing algorithm, called objective classication sharing algorithm(OCSA) that can preserve the diversity of the population. We will show the superior performance of the proposed algorithm by comparing the proposed algorithm with other promising algorithms for the test functions.

• 한국지능시스템학회논문지
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• 제17권7호
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• pp.869-874
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• 2007

다목적 함수의 최적화 문제(Multiobjective optimization problems)의 경우에는 하나의 최적해가 존재하는 것이 아니라 '파레토 최적해 집합(Pareto optimal set)'이라고 알려진 해들의 집합이 존재한다. 이러한 이상적 파레토 최적해 집합과 가까운 최적해를 찾기 위한 다양한 해탐색 능력은 진화 알고리즘의 성능을 결정한다. 본 논문에서는 게임 모델에 기반한 공진화 알고리즘(GCEA: Game model based Co-Evolutionary Algorithm)에서 해집단의 다양성을 유지하여, 다양한 비지배적 파레토 대안해(non-dominated alternatives)들을 찾기 위한 방법을 제안한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.229-232
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• 2001

Recently, several promising multiobjective evolutionary algorithms, e,g, SPEA, NSGA-ll, PESA, and SPEA2, have been developed. In this paper, we also propose a new multiobjective evolutionary algorithm that compares to them. In the algorithm proposed in this paper, we introduce a novel concept, "inherited age" and total algorithm is executed based on the inherited age concept. Also, we propose a new sharing algorithm, called objective classication sharing algorithm(OCSA) that can preserve the diversity of the population. We will show the superior performance of the proposed algorithm by comparing the proposed algorithm with other promising algorithms for the test functions.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.816-821
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• 2003

Researchers have utilized artificial evolution techniques and learning techniques for studying the interactions between learning and evolution. Adaptation in dynamic environments gains a significant advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot navigations. We support our method, based on (1+1) evolutionary strategy which produces changes during the lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on evolutionary neural controller for physical robots behaviors. We investigate the effects of learning in evolutionary process by comparing the performance of the proposed realtime evolutionary learning method with that of evolutionary method only. Also, we investigate an interactive evolutionary algorithm to overcome the difficulties in evaluating complicated tasks.