한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제21권2호
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  • Pages.206-211
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  • 2011
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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연속탐색공간에 대한 진화적 해석

Evolutionary Analysis for Continuous Search Space

  • 이준성 (경기대학교 기계시스템공학과) ;
  • 배병규 (경기대학교 대학원 기계공학과)
  • 투고 : 2011.01.19
  • 심사 : 2011.04.13
  • 발행 : 2011.04.25
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초록

본 논문에서는 연속적인 파라미터 공간에 대한 최적화에 대해 진화적 알고리즘의 특징적인 형상화를 제시한다. 이 방법은유전알고리즘이 연속적인 탐색공간에서의 파라미터 식별에 대해 가장 강점을 지녔다는 점에 착안한 것이다. 유전알고리즘과 제안한 알고리즘과의 주요한 차이점은 개별적 또는 연속적인 묘사의 차이가 있다는 것이다. 잘 알려진 실험함수의 최적화문제를 도입하여 연속 탐색공간 문제에 대해 제안하는 알고리즘에 대해 계산시간 및 사용메모리 등의 성능이 우수하다는 효율성을 보였다.

In this paper, the evolutionary algorithm was specifically formulated for optimization with continuous parameter space. The proposal was motivated by the fact that the genetic algorithms have been most intensively reported for parameter identification problems with continuous search space. The difference of primary characteristics between genetic algorithms and the proposed algorithm, discrete or continuous individual representation has made different areas to which the algorithms should be applied. Results obtained by optimization of some well-known test functions indicate that the proposed algorithm is superior to genetic algorithms in all the performance, computation time and memory usage for continuous search space problems.

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