원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.7
2016.07
pp.361-372
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Genetic algorithms for multi-objective optimization problem to be solved were studied. Through the elitist strategy analysis, it is an improved multi-objective optimization algorithm. The algorithm uses a data warehouse to store the optimal solution produced by individuals in each generation, from the way individuals adopt measures to phase out the individual data warehouse identical or similar, the algorithm also improved selection operator, so that the algorithm adaptive capacity enhancement, the new algorithm improves the algorithm performance, improves the quality of understanding between sets, can get a lot of optimal and balanced.
목차
Abstract
1. Introduction
2. Related Works
3. Mathematical Models of Genetic Algorithms
3.1. Basic Definition of Multi-Objective Optimization
3.2. Right Weight Coefficient of Genetic Algorithm
4. System Assessments
4.1. Fitness Function and Operator
4.2. Genetic Algorithm Design and Implementation
4.3. Application Example
5. Conclusions
References
1. Introduction
2. Related Works
3. Mathematical Models of Genetic Algorithms
3.1. Basic Definition of Multi-Objective Optimization
3.2. Right Weight Coefficient of Genetic Algorithm
4. System Assessments
4.1. Fitness Function and Operator
4.2. Genetic Algorithm Design and Implementation
4.3. Application Example
5. Conclusions
References
저자정보
참고문헌
자료제공 : 네이버학술정보