원문정보
보안공학연구지원센터(IJGDC)
International Journal of Grid and Distributed Computing
vol.2 no.3
2009.09
pp.25-32
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Generally speaking, genetic algorithms are simulations of evolution, of what kind ever. In most cases, however, genetic algorithms are nothing else than probabilistic optimization methods which are based on the principles of evolution. This tutorial covers application oriented study of genetic algorithms as in the case of Eye Location Using Genetic Algorithm; Using simulation and Genetic Algorithms to improve cluster tool performance; Mooring Pattern Optimization using Genetic Algorithms. This tutorial is designed to cover a few important applicational aspects of genetic algorithm under a single umbrella.
목차
Abstract
1. Introduction
2. Basic steps of a Genetic Algorithm
3. Analysis of existing Algorithms
3.1 Eye Location Using Genetic Algorithm
3.2 Using Simulation and Genetic Algorithms to Improve Cluster Tool Performance
3.3 Mooring Pattern Optimization using Genetic Algorithms
4. Discussion
4.1 Eye Location Using Genetic Algorithm
4.2 Using Simulation and Genetic Algorithms to Improve Cluster Tool Performance
4.3 Mooring Pattern Optimization using Genetic Algorithms
5. Conclusion
References
1. Introduction
2. Basic steps of a Genetic Algorithm
3. Analysis of existing Algorithms
3.1 Eye Location Using Genetic Algorithm
3.2 Using Simulation and Genetic Algorithms to Improve Cluster Tool Performance
3.3 Mooring Pattern Optimization using Genetic Algorithms
4. Discussion
4.1 Eye Location Using Genetic Algorithm
4.2 Using Simulation and Genetic Algorithms to Improve Cluster Tool Performance
4.3 Mooring Pattern Optimization using Genetic Algorithms
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보