Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Function Optimization and Event Clustering by Adaptive Differential Evolution

적응성 있는 차분 진화에 의한 함수최적화와 이벤트 클러스터링

  • 황희수 (한라대학교 전기전자제어공학부)
  • Published : 2002.10.01
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Abstract

Differential evolution(DE) has been preyed to be an efficient method for optimizing real-valued multi-modal objective functions. DE's main assets are its conceptual simplicity and ease of use. However, the convergence properties are deeply dependent on the control parameters of DE. This paper proposes an adaptive differential evolution(ADE) method which combines with a variant of DE and an adaptive mechanism of the control parameters. ADE contributes to the robustness and the easy use of the DE without deteriorating the convergence. 12 optimization problems is considered to test ADE. As an application of ADE the paper presents a supervised clustering method for predicting events, what is called, an evolutionary event clustering(EEC). EEC is tested for 4 cases used widely for the validation of data modeling.

차분 진화는 다양한 형태의 목적함수를 최적화하는데 매우 효율적인 방법임이 입증되었다 차분 진화의 가장 큰 이점은 개념적 단순성과 사용의 용이성이다. 그러나 차분 진화의 수렴성이 제어 파라미터에 매우 민감한 단점이 있다. 본 논문은 새로운 교배용 벡터 생성법과 제어 파라미터의 적응 메커니즘을 결합한 적응성 있는 차분 진화를 제안한다. 이는 수렴성을 해치지 않으면서 차분 진화를 보다 강인하게 만들며 사용이 쉽도록 해준다. 12가지 최적화 문제에 대해 제안한 방법을 시험하였다. 적응성 있는 차분 진화의 응용 사례로써 이벤트 예측을 위한 교사 클러스터링 방법을 제안한다. 이 방법을 진화에 의한 이벤트 클러스터링이라 부르며 데이터 모델링 검증에 널리 사용되는 4 가지 사례에 대해 그 성능을 시험하였다.

Keywords

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