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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2009.8
- 수록면
- 2,579 - 2,584 (6page)
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초록· 키워드
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The survey of the relevant literature showed that the rehave been many studies for portfolio optimization problem and that the number of studies which have investigated the optimum portfoliousing evolutionary computation is quite high. But almost none of these studies deals with genetic relation algorithm(GRA). This study presents an approach to large-scale port folio optimization problem using GRA with a new operator, called guided mutation. In order to pickup the most efficient portfolio, GRA considers the correlation coefficient between stock brands as strength, which indicates there lation between nodes in each individual of GRA. Guided mutation generates off spring according to the average value of correlation coefficients in each individual. A genetic relation algorithm with guided mutation(GRA/G) for the portfolio optimization is proposed in this paper. Genetic network programming(GNP), which was proposed in our previous research, is used to validate the performance of the portfolio generated with GRA/G. The results show that GRA/Gap proach is successful in portfolio optimization.
목차
Abstract
1.INTRODUCTION
2.GENETIC RELATION ALGORITHM WITH GUIDED MUTATION
3.PORTFOLIO SELECTION USING GRA/G
4.EXPERIMENTAL RESULTS
5.CONCLUSIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2014-569-000766986