Research on a New Genetic Algorithm Model in Cloud Computing
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- 발행기관
- 보안공학연구지원센터(IJGDC) 바로가기
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- 간행물
- International Journal of Grid and Distributed Computing SCOPUS 바로가기
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- 통권
- Vol.9 No.12 (2016.12)바로가기
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- 페이지
- pp.63-74
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A297048
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- As to the problems that can been solved by genetic algorithm in Big Data, genetic algorithm research has profound significance. To meet the need of social development, many researchers have been doing a lot of work in thought and theory of that and come up with specific thought and theory. In the paper, firstly look up papers about the implementation rationale, model and specific implementation of genetic algorithm in HADOOP. Summarise two implementation models of genetic algorithm in HADOOP, vertical strategy and horizontal strategy. And deeply analyze the two implementation models, their design and implementation. Based on above, come up with improved model of genetic algorithm in HADOOP, its detailed design and general implementation steps. The improved implementation model is tested by test data to verify its correctness and validity. Experimental results prove to be that the improved implementation model of this paper is correct and valid. Finally, the improved implementation model of this paper is applied to solving facility location of emergency system.
목차
Abstract
1. Introduction
2. Implementation of Genetic Algorithm in Cloud Computing
2.1 Vertical Strategy Implementation Model of Genetic Algorithm
2.2 Horizontal Strategy Implementation Model of Genetic Algorithm
3. Implementation Model of Improved Genetic Algorithm in Cloud Computing
3.1. Analyze Implementation Model of Existing Genetic Algorithm in Cloud Computing
3.2. Implementation Model of the Improved Genetic Algorithm
3.3 Map/Reduce Communication Scheme of Implementation Model of Improved Genetic Algorithm at Each Stage
3.4 Steps for Solution Problem by the Implementation Model of the ImprovedGenetic Algorithm
4. Experimental Analysis and Results
4.1Experimental Data and Experimental Platform
4.2 Experimental results and analysis
5. Conclusion
References
키워드
Cloud Computing Big Data HADOOP Genetic Algorithm저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJGDC) [Science & Engineering Research Support Center, Republic of Korea(IJGDC)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Grid and Distributed Computing
- 간기
- 격월간
- pISSN
- 2005-4262
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Grid and Distributed Computing Vol.9 No.12
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