• Title, Summary, Keyword: Evolutionary Algorithm

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Design of Fuzzy Controller Using Parasitic Co-evolutionary Algorithm (기생적 공진화 알고리즘을 이용한 퍼지 제어기 설계)

  • 심귀보;변광섭
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.11
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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.

A Study on the Quadratic Multiple Container Packing Problem (Quadratic 복수 컨테이너 적재 문제에 관한 연구)

  • Yeo, Gi-Tae;Soak, Sang-Moon;Lee, Sang-Wook
    • Journal of the Korean Operations Research and Management Science Society
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    • v.34 no.3
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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.

Optimal Design of the 2-Layer Fuzzy Controller using the Schema Co-Evolutionary Algorithm (Schema Co-Evolutionary Algorithm을 이용한 2-Layer Fuzzy Controller의 최적 설계)

  • Sim, Kwee-Bo;Byun, Kwang-Sub
    • Journal of Korean Institute of Intelligent Systems
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    • v.14 no.2
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    • pp.228-233
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    • 2004
  • Nowadays, the robot with various and complex functions is required. previous algorithms, however, cannot satisfy the requirement. In order to solve these problems, we introduce the 2-Layer Fuzzy Controller, which has a small number of fuzzy rules corresponding to various inputs and outputs. Also, it controls robustly and effectively an object. The main problem in the fuzzy controller is how to design the fuzzy rule. This paper designs the optimal 2-layer fuzzy controller using the Schema Co-Evolutionary Algorithm. The schema co-evolutionary algorithm can find more rapidly and excellently than simple genetic algorithm does.

An Evolutionary Algorithm for Determining the k Most Vital Arcs in Shortest Path Problem (최단경로문제에서 k개의 치명호를 결정하는 유전알고리듬)

  • 정호연
    • Journal of the military operations research society of Korea
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    • v.26 no.2
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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.

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A New evolutionary Multiobjective Optimization Algorithm based on the Non-domination Direction Information (비지배 방향정보를 이용한 새로운 다목적 진화 알고리즘)

  • Kang, Young-Hoon;Zeungnam Bien
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.103-106
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    • 2000
  • In this paper, we introduce a new evolutionary multiobjective optimization algorithm based on the non-domination direction information, which can be an alternative among several multiobjective evolutionary algorithms. The new evolutionary multiobjective optimization algorithm proposed in this paper will not use the conventional recombination or mutation operators but use the non-domination directions, which are extracted from the non-domination relation among the population. And the problems of the modified sharing algorithms are pointed out and a new sharing algorithm sill be proposed to overcome those problems.

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Game Model Based Co-evolutionary Solution for Multiobjective Optimization Problems

  • Sim, Kwee-Bo;Kim, Ji-Yoon;Lee, Dong-Wook
    • International Journal of Control, Automation, and Systems
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    • v.2 no.2
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    • pp.247-255
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    • 2004
  • The majority of real-world problems encountered by engineers involve simultaneous optimization of competing objectives. In this case instead of single optima, there is a set of alternative trade-offs, generally known as Pareto-optimal solutions. The use of evolutionary algorithms Pareto GA, which was first introduced by Goldberg in 1989, has now become a sort of standard in solving Multiobjective Optimization Problems (MOPs). Though this approach was further developed leading to numerous applications, these applications are based on Pareto ranking and employ the use of the fitness sharing function to maintain diversity. Another scheme for solving MOPs has been presented by J. Nash to solve MOPs originated from Game Theory and Economics. Sefrioui introduced the Nash Genetic Algorithm in 1998. This approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an Evolutionary Stable Strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. First, we will investigate the validity of our co-evolutionary approach to solve MOPs. That is, we will demonstrate how the evolutionary game can be embodied using co-evolutionary algorithms and also confirm whether it can reach the optimal equilibrium point of a MOP. Second, we will evaluate the effectiveness of our approach, comparing it with other methods through rigorous experiments on several MOPs.

Optimal Design of a 2-Layer Fuzzy Controller Using the Schema Co-Evolutionary Algorithm

  • Byun, Kwang-Sub;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.3
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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.

Optimal Design of a 2-Layer Fuzzy Controller using the Schema Co-Evolutionary Algorithm

  • Park Chang-Hyun;Sim Kwee-Bo
    • International Journal of Control, Automation, and Systems
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    • v.3 no.3
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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.

The Development of a New Distributed Multiobjective Evolutionary Algorithm with an Inherited Age Concept (계승적 나이개념을 가진 다목적 진화알고리즘 개발)

  • 강영훈;변증남
    • Journal of Korean Institute of Intelligent Systems
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    • v.11 no.8
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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.

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Study on Diversity of Population in Game model based Co-evolutionary Algorithm for Multiobjective optimization (다목적 함수 최적화를 위한 게임 모델에 기반한 공진화 알고리즘에서의 해집단의 다양성에 관한 연구)

  • Lee, Hea-Jae;Sim, Kwee-Bo
    • Journal of Korean Institute of Intelligent Systems
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    • v.17 no.7
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    • pp.869-874
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    • 2007
  • In searching for solutions to multiobjective optimization problem, we find that there is no single optimal solution but rather a set of solutions known as 'Pareto optimal set'. To find approximation of ideal pareto optimal set, search capability of diverse individuals at population space can determine the performance of evolutionary algorithms. This paper propose the method to maintain population diversify and to find non-dominated alternatives in Game model based Co-Evolutionary Algorithm.