Earticle

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

키워드

저자

• Deep Malya Mukhopadhyay [ Heritage Institute of Technology ]
• Maricel O. Balitanas [ Hannam University ]
• Alisherov Farkhod A [ Hannam University ]
• Seung-Hwan Jeon [ Hannam University ]
• Debnath Bhattacharyya [ Heritage Institute of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605