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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2008.5
- 수록면
- 1,172 - 1,179 (8page)
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이 논문의 연구 히스토리 (2)
2008
Adaptive Genetic Algorithm with Local Search for Solving Reliability Optimization Design Problems
YoungSu Yun
,
Chiung Moon
한국경영과학회 학술대회논문집
2008.05
학술대회자료
Adaptive Genetic Algorithm with Local Search for Solving Reliability Optimization Design Problems
YoungSu Yun
,
Chiung Moon
대한산업공학회 춘계공동학술대회 논문집
2008.05
학술대회자료
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초록· 키워드
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This paper suggests an adaptive genetic algorithm with local search (aGA-ls) for efficient solving reliability optimization design problems. The aGA-ls has the adaptive scheme which can automatically determine the use of local search technique and adaptively regulate the rate of crossover and mutation operations in genetic algorithm (GA) during its search process. For local search technique, an improved iterative hill climbing method is used and incorporated into GA loop. The adaptive scheme used in the aGA-ls considers the change of the variances resulting from various fitness values of population in each generation.
In order to demonstrate the efficiency of aGA-ls, we also consider i) a canonical GA without adaptive scheme and local search technique, ii) a canonical GA with local search technique alone, and iii) several adaptive GAs (aGAs) with various adaptive schemes. These algorithms, including aGA-ls, have been tested and analyzed in application to a variety of reliability optimization design problems. Numeric performance measures show that the proposed aGAls outperforms conventional algorithms.
In order to demonstrate the efficiency of aGA-ls, we also consider i) a canonical GA without adaptive scheme and local search technique, ii) a canonical GA with local search technique alone, and iii) several adaptive GAs (aGAs) with various adaptive schemes. These algorithms, including aGA-ls, have been tested and analyzed in application to a variety of reliability optimization design problems. Numeric performance measures show that the proposed aGAls outperforms conventional algorithms.
목차
Abstract
1. Introduction
2. Reliability Optimization Design Problems
3. Exploitation and Exploration in GA Search
4. aGA-ls Design
5. Numerical Examples
6. Conclusion
References
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