• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.347-350
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• 2008

모듈라 뱀형 로봇은 고장에 대한 강인성과 환경에 유연한 이동 특성을 가지고 있으나, 제어가 어렵다는 단점이 있다. 진화연산을 로봇에 이용한 많은 연구가 진행되어 왔지만, 어떤 기법의 진화연산이 문제에 더 적합하고, 높은 성능을 얻을 수 있는지에 대한 비교는 거의 이루어지지 않고 있다. 본 논문은 두 가지 대표적인 진화기법인 GA와 GP를 이용하여 모듈라 뱀형 로봇의 이동 제어를 수행하였다. 대상 로봇은 H/W로 구현이 가능한 실제 모듈로 구성되었고, Webots을 사용하여 시뮬레이션 실험을 수행하였으며, GA와 GP 기법에 의한 결과를 비교 분석하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.292-294
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• 2011

전기자동차에 대한 연구가 진행됨에 따라 동력원으로 사용되는 대용량 리튬-폴리머 배터리의 운용과 관리에 대한 관심이 증가하고 있다. 다중 셀로 구성된 대용량 리튬-폴리머 배터리는 물리적 화학적 특성에 따라 충전시 셀간 전압 격차가 발생하게 된다. 셀간 전압차는 배터리 용량, 수명, 안정성에 부정적 영향을 주게 된다. 기존 연구들은 각 셀의 특성을 고려하지 않고 충전 결과를 바탕으로 동일한 밸런싱 방법을 적용시킴으로 효율성을 떨어트린다. 본 논문에서는 진화 신경망 기반의 지능형 셀 밸런싱 시스템을 제안한다. 배터리의 특성을 진화 신경망을 통해 학습시킴으로 각 셀 충전시 저항의 크기를 결정한다. 이를 통해 각 셀 특성을 고려한 사전 셀 밸런싱을 수행하였다. 제안하는 방법의 유용성을 입증하기 위해 카이스트 온라인 전기자동차에 장착 예정인 배터리 관리 시스템 기반 시뮬레이션을 수행하여 효과적인 셀 밸런싱이 가능함을 보였다.

• Electronics and Telecommunications Trends
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• v.13 no.1 s.49
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• pp.14-20
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• 1998

본 논문은 대도시 전화망에서 루팅 효과, 통화량에 준한 망설계기법에서 전송시설의 경제적 규모, 그리고 투자비용 변화를 분석하기 위한 시뮬레이션 모델 개발에 관한 것이다. 모의시험 결과는 전화망에서 교환/전송에 소요되는 비용은 루팅방식의 변경에 따라서 크게 영향을 받으며, 고속 전송로 사용에 따른 규모의 경제효과가 클수록 망 구축에 소요되는 비용이 현저히 작아지는 것으로 나타났다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.632-637
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• 2008

In real system application, the propeller shaft rate (PSR) estimation algorithm for the feature extraction of the sonar target operates with the following problems: it requires both accurate and efficient the fundamental finding method because it is essential and difficult to distinguish harmonic family composed of the fundamental and its harmonics from the multiple spectral lines in the frequency spectrum-based sonar target classification, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an evolutionary PSR estimation algorithm using an expert knowledge and the evolution strategy, is proposed. To verify the performance of the proposed algorithm, a sonar target PSR estimation is performed. Simulation results show that the proposed algorithm effectively solves the problems in the realtime system application.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.231-238
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• 2002

As a modeling method where the merits of fuzzy inference system and evolutionary computation are put together, evolutionary fuzzy modeling performs global approximate optimization. The paper proposes fuzzy clustering as fuzzy rule generation process which is one of the most important steps in evolutionary fuzzy modeling. With application of fuzzy clustering into the experiment or simulation results, fuzzy rules which properly describe non-linear and complex design problem can be obtained. The efficiency of evolutionary fuzzy modeling can be improved utilizing the membership degrees of data to clusters from the results of fuzzy clustering. To ensure the validity of the proposed method, the real design problem of an automotive inner trim is applied and the global approximation is achieved. Evolutionary fuzzy modeling is performed for several cases which differ in the number of clusters and the criterion of rule selection and their results are compared to prove that the proposed method can provide proper fuzzy rules for a given system and reduce computation time while maintaining the errors of modeling as a satisfactory level.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.149-152
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• 2007

본 논문에서는 대규모 네트워크 상에서 발생되는 시뮬레이션 결과를 효과적으로 보여주기 위한 Visualization Tool 을 제안한다. 복잡하고 다양한 시뮬레이션 결과를 얻기 위해, 생태계 모방형 플랫폼을 이용한 Bio-IDS (Intrusion Detection System) 시뮬레이터의 실험 데이터를 이용하였다. 대규모 네트워크를 모두 보이기에는 화면이 너무 작기 때문에, Visualization Tool 은 화면의 확대 및 축소를 위한 Zoom In/Out 기능, 화면의 Panning 을 위한 Scroll Bar 및 현재 영역의 위치를 알려주는 Mini Map 이 필요하였다. 또한, 사용자가 쉽게 시뮬레이션의 속도를 조절할 수 있도록 Simulation Speed Control 기능을 구현하였으며, 각 노드의 효과적인 정상 및 침입 상태 표시를 위한 Icon, 각 노드의 진화 정도와 침입 탐지 정확도를 알려주는 Evolution Number와 Accuracy Gauge, 해당 시뮬레이션의 결과를 도시하기 위한 Simulation Graph 도 추가하였다. 네트워크 Off-line 환경도 대비하여, DB 로부터의 데이터 입력뿐만 아니라 Log File 을 통한 데이터 입력도 가능하게 하였다. 끝으로, 전체 Node 들의 다양한 상태변화를 확인할 수 있는 Topology Window 와 Simulation Demo Window 간의 Synchronization 을 위한 Socket 통신 등 다양한 기능들이 통합된 Visualization Tool 을 개발함으로써, 대규모 네트워크 시뮬레이션의 효과적인 시뮬레이션이 가능하게 되었다. 이로 인해 대규모 네트워크 상의 복잡한 시뮬레이션 결과도 사용자가 매우 쉽게 파악할 수 있 매우 효과적으로 사용자가 파악할 수 있게 되었다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.27-35
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• 2021

Artificial life is used in various fields of applied science by evaluating natural life-related systems, their processes, and evolution. Research has been actively conducted to evolve physical body design and behavioral control strategies for the dynamic activities of these artificial life forms. However, since co-evolution of shapes and neural networks is difficult, artificial life with optimized movements has only one movement in one form and most do not consider the environmental conditions around it. In this paper, artificial life that co-evolve bodies and neural networks using predator-prey models have environmental adaptive movements. The predator-prey hierarchy is then extended to the top-level predator, medium predator, prey three stages to determine the stability of the simulation according to initial population density and correlate between body evolution and population dynamics.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.293-303
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• 2016

Analysis of bargaining games utilizing evolutionary computation in recent years has dealt with important issues in the field of game theory. In this paper, we investigated interaction and coevolution process among heterogeneous artificial agents using evolutionary computation in the bargaining game. We present three kinds of evolving-strategic agents participating in the bargaining games; genetic algorithms (GA), particle swarm optimization (PSO) and differential evolution (DE). The co-evolutionary processes among three kinds of artificial agents which are GA-agent, PSO-agent, and DE-agent are tested to observe which EC-agent shows the best performance in the bargaining game. The simulation results show that a PSO-agent is better than a GA-agent and a DE-agent, and that a GA-agent is better than a DE-agent with respect to co-evolution in bargaining game. In order to understand why a PSO-agent is the best among three kinds of artificial agents in the bargaining game, we observed the strategies of artificial agents after completion of game. The results indicated that the PSO-agent evolves in direction of the strategy to gain as much as possible at the risk of gaining no property upon failure of the transaction, while the GA-agent and the DE-agent evolve in direction of the strategy to accomplish the transaction regardless of the quantity.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.65-73
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• 1997

This paper proposes a new method of the structure pruning of dynamic recurrent neural networks (DRNN) using evolutionary computations. In general, evolutionary computations are population-based search methods, therefore it is very useful when several different properties of neural networks need to be optimized. In order to prune the structure of the DRNN in this paper, we used the evolutionary programming that searches the structure and weight of the DRNN and evolution strategies which train the weight of neuron and pruned the net structure. An addition or elimination of the hidden-layer's node of the DRNN is decided by mutation probability. Its strategy is as follows, the node which has mhnimum sum of input weights is eliminated and a node is added by predesignated probability function. In this case, the weight is connected to the other nodes according to the probability in all cases which can in- 11:ract to the other nodes. The proposed pruning scheme is exemplified on the stabilization and position control of the inverted-pendulum system and visual servoing of a robot manipulator and the effc: ctiveness of the proposed method is demonstrated by numerical simulations.

• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.257-265
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• 2000

There is much selective confliction in nature where selfish and rational individuals exists. Iterated Prisoner's Dilemma (IPD) game deals with this problem, and has been used to study on the evolution of cooperation in social, economic and biological systems. So far, there has been much work about the relationship of the number of players and cooperation, strategy learning as a machine learning and the effect of payoff functions to cooperation. In this paper, We attempt to investigate the cooperative coalition size according to payoff functions, and observe the relationship of localization and the evolution of cooperation in NIPD (N-player IPD) game. Experimental results indicate that cooperative coalition size increases as the gradient of the payoff function for cooperation becomes steeper than that of defector's payoff function, or as the minimum coalition size gets smaller, Moreover, the smaller the neighborhood of interaction is, the higher the cooperative coalition emerges through the evolution of population.