Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Smallest-Small-World Cellular Genetic Algorithms

최소좁은세상 셀룰러 유전알고리즘

  • 강태원 (강릉대학교 컴퓨터공학과)
  • Published : 2007.11.15
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Abstract

Cellular Genetic Algorithms(CGAs) are a subclass of Genetic Algorithms(GAs) in which each individuals are placed in a given geographical distribution. In general, CGAs# population space is a regular network that has relatively long characteristic path length and high clustering coefficient in the view of the Networks Theory. Long average path length makes the genetic interaction of remote nodes slow. If we have the population#s path length shorter with keeping the high clustering coefficient value, CGAs# population space will converge faster without loss of diversity. In this paper, we propose Smallest-Small-World Cellular Genetic Algorithms(SSWCGAs). In SSWCGAs, each individual lives in a population space that is highly clustered but having shorter characteristic path length, so that the SSWCGAs promote exploration of the search space with no loss of exploitation tendency that comes from being clustered. Some experiments along with four real variable functions and two GA-hard problems show that the SSWCGAs are more effective than SGAs and CGAs.

셀룰러 유전알고리즘(CGAs)은 모집단이 특정한 위상 구조를 갖는 유전알고리즘의 일종이다. 보통의 경우, CGAs의 모집단 공간은 네트워크 이론 측면에서 상대적으로 긴 평균경로길이와 큰 클러스터링계수를 갖는 정규 격자형 위상 구조이다. 평균경로길이가 길면 멀리 떨어진 개체들 사이의 유전적 상호작용이 느리게 일어난다. 따라서 클러스터링계수를 유지하면서 평균경로길이를 줄인다면 개체의 다양성이 유지되면서도 모집단이 보다 빠르게 수렴할 것이다. 이 논문에서는 최소좁은세상 셀룰러 유전알고리즘(SSWCGAs)을 제안한다. SSWCGAs에서 각 개체는 클러스터링이 잘되었으면서도 노드를 연결하는 평균경로길이가 짧은 모집단에 거주하여, 클러스터링에 의한 세부탐색 능력을 유지하면서도 전역탐색을 잘하게 된다. 네 가지 실변수 함수와 두 가지 GA-hard 문제에 대한 실험을 통하여 SSWCGAs가 SGAs 및 CGAs보다 효과적임을 보였다.

Keywords

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