Earticle

초록

영어

This paper compares the performance of Genetic Algorithm and Population-based Incremental Learning algorithm on a student ranking application. The proposed student ranking application focuses on proving the students’ achievements in fields like attendance, achievements and backlogs apart from academics. This application produces the output according to the prioritized weights inputted by the user. Genetic Algorithm and Population-based Incremental Learning is evolutionary search based optimization algorithm which can arrive at near-optimal solution faster by using randomized techniques. These are stochastic search based algorithms that include randomness to escape local optima. The two algorithms are compared based on the accuracy of optimal value, number of generations and time taken to reach optimal value.

목차

Abstract
 1. Introduction
  1.1. Student Ranking Application
 2. Optimization
 3. Genetic Algorithm
  3.1. Encoding
  3.2. Selection
  3.3. Crossover (Recombination)
  3.4. Mutation
  3.5. Decoding
 4. Population-based Incremental Learning
 6. Experimentation and Results
  6.1. Comparison of Genetic Algorithm and Population-based Incremental Learning Algorithm
 References

키워드

저자

• Ch. Viswanathsarma [ Department of Computer Science and Engineering, Vignan’s Institute of Information technology Visakhapatnam-530049, AP, India ]
• Debnath Bhattacharyya [ Department of Computer Science and Engineering, Vignan’s Institute of Information technology Visakhapatnam-530049, AP, India ]
• Hye-jin Kim [ Sungshin Women's University, Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605