• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.207-210
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• 2002

진화와 학습 사이의 상호 연관성을 연구하기 위해 인공 진화기법(artificial evolutionary algorithm)과 신경회로망(neural networks)을 이용한 학습 기법들이 사용되어 왔다. 신경 회로망 구조를 가지는 이동 로봇의 제어기의 구조와 파라미터를 결정하기 위한 방법으로 진화적 학습(evolutionary learning) 방법이 제안되었다. 제안된 방법에서 진화적 학습은 실제 로봇을 통해 on-line 방식으로 이루어지며, 장애물 회피 문제를 통해 유용성을 검증하고 진화 과정에 학습이 미치는 영향을 살펴보았다. 그리고 수학적으로 제시되기 힘든 진화 학습의 평가에 설계자의 개입을 허용하는 인터액티브 진화 알고리즘(interactive evolutionary algorithm)방법을 모색해 보았다.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.10-19
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• 2005

The minimum spanning tree (MST) problem is one of the traditional optimization problems. Unlike the MST, the degree constrained minimum spanning tree (DCMST) of a graph cannot, in general, be found using a polynomial time algorithm. So, finding the DCMST of a graph is a well-known NP-hard problem of importance in communications network design, road network design and other network-related problems. So, it seems to be natural to use evolutionary algorithms for solving DCMST. Especially, when applying an evolutionary algorithm to spanning tree problems, a representation and search operators should be considered simultaneously. This paper introduces a new tree representation scheme and a genetic operator for solving combinatorial tree problem using evolutionary algorithms. We performed empirical comparisons with other tree representations on several test instances and could confirm that the proposed method is superior to other tree representations. Even it is superior to edge set representation which is known as the best algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.491-496
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• 2002

Multi-objective Optimization Problems(MOPs) are occur more frequently than generally thought when we try to solve engineering problems. In the real world, the majority cases of optimization problems are the problems composed of several competitive objective functions. In this paper, we introduce the definition of MOPs and several approaches to solve these problems. In the introduction, established optimization algorithms based on the concept of Pareto optimal solution are introduced. And contrary these algorithms, we introduce theoretical backgrounds of Nash Genetic Algorithm(Nash GA) and Evolutionary Stable Strategy(ESS), which is the basis of Co-evolutionary algorithm proposed in this paper. In the next chapter, we introduce the definitions of MOPs and Pareto optimal solution. And the architecture of Nash GA and Co-evolutionary algorithm for solving MOPs are following. Finally from the experimental results we confirm that two algorithms based on Evolutionary Game Theory(EGT) which are Nash GA and Co-evolutionary algorithm can search optimal solutions of MOPs.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.79-86
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• 2005

The transportation problem (TP) is one of the traditional optimization problems. Unlike the TP, the fixed charge transportation problem (FCTP) cannot be solved using polynomial time algorithms. So, finding solutions for the FCTP is a well-known NP-complete problem involving an importance in a transportation network design. So, it seems to be natural to use evolutionary algorithms for solving FCTP. And many evolutionary algorithms have tackled this problem and shown good performance. This paper introduces an efficient evolutionary algorithm for the FCTP. The proposed algorithm can always generate feasible solutions without any repair process using the random key representation. Especially, it can guide the search toward the basic solution. Finally, we performed comparisons with the previous results known on the benchmark instances and could confirm the superiority of the proposed algorithm.

• Proceedings of the Korea Society for Simulation Conference
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• 1997.04a
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• pp.124-127
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• 1997

A key element in the control of Automated Guided Vehicle Systems (AGVS) is dispatching policy. This paper proposes a new dispatching algorithm for an efficient operation of AGVS. Based on an evolutionary operation, it has an adaptive control capability responding to changes of the system environment. The performance of the algorithm is compared with some well-known dispatching rules in terms of the system throughput through simulation. Sensitivity analysis is carried out varying the buffer capacity and the number of AGVS.

• Industrial Engineering and Management Systems
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• v.10 no.3
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• pp.203-208
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• 2011

Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.503-506
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• 2005

In this study, optimization design technique for control of solid-fluid coupled force (sloshing) using evolutionary method is suggested. Artificial neural networks(ANN) and genetic algorithm(GA) is employed as evolutionary optimization method. The ANN is used to analysis of the sloshing and the genetic algorithm is adopted as an optimization algorithm. In the creation of ANN learning data, the design of experiments is adopted to higher performance of the ANN learning using minimum learning data and ALE(Arbitrary Lagrangian Eulerian) numerical method is used to obtain the sloshing analysis results. The proposed optimization technique is applied to the minimization of sloshing of the water in the tank lorry with baffles under 2 second lane change.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.191-196
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• 2008

This paper presents an evolutionary algorithm for goal programming with preemptive priority. To do this, an evolutionary strategy is suggested which search for the solution satisfying the goals in the order of the priority. Two-sided assembly line balancing problems with multiple goals are used to validate the applicability of the algorithm. In the problems, three goals are considered in the following priority order: minimizing the number of mated-stations, achieving the goal level of workload smoothness, and maximizing the work relatedness. The proper evolutionary components such as encoding and decoding method, evaluation scheme, and genetic operators, which are specific to the problem being solved, are designed in order to improve the algorithm's performance. The computational result is reported.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.5
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• pp.205-211
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• 2000

This paper presents an optimal design method for fuzzy logic controller (FLC) of HVDC using an Adaptive Evolutionary Algorithm(AEA). We have proposed the AEA which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary algorithms. The AEA is used for tuning fuzzy membership functions and scaling constants. Simulation results show that disturbances are well damped and the dynamic performances of FLC have better responses than those of PD controller when AC system load changes suddenly.