Multi-objective genetic algorithm in solving conflicted goals for questions generating problem

Nur Suhailayani Suhaimi, Siti Nur Kamaliah Kamarudin, Zalinda Othman, Norazam Arbin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Multi-objective genetic algorithm (MOGA) has been used for more than a decade to solve real-world optimization problems that have several, and often conflicting objectives. In this research, the conflicting objectives of achieving the maximum accuracy of the solution and at the same time minimizing the redundancy of the optimal solutions in retrieving the best set of exam questions for academicians for a particular subject are highlighted. Hence, the aim of this paper is to solve the multi-objective problem in a chromosome (solution) and also to maintain the fitness of the chromosome. The results of this research are measured based on the similarity achieved between the obtained and desired solutions. By using MOGA, a promising result is obtained with the maximum accuracy and simultaneously, minimizing the redundancy of the genes in a solution.

Original languageEnglish
Title of host publicationProceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS
PublisherIEEE Computer Society
Pages60-63
Number of pages4
Volume2015-September
ISBN (Print)9781479938575
DOIs
Publication statusPublished - 28 Sep 2015
Event5th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2014 - Langkawi, Malaysia
Duration: 27 Jan 201429 Jan 2014

Other

Other5th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2014
CountryMalaysia
CityLangkawi
Period27/1/1429/1/14

Fingerprint

Multi-objective Genetic Algorithm
Chromosome
Redundancy
Genetic algorithms
Chromosomes
Fitness
Optimal Solution
Optimization Problem
Gene
Genes
Similarity

Keywords

  • Genetic algorithm
  • MOGA
  • Multi-objectives

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Modelling and Simulation
  • Theoretical Computer Science

Cite this

Suhaimi, N. S., Kamarudin, S. N. K., Othman, Z., & Arbin, N. (2015). Multi-objective genetic algorithm in solving conflicted goals for questions generating problem. In Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS (Vol. 2015-September, pp. 60-63). [7280879] IEEE Computer Society. https://doi.org/10.1109/ISMS.2014.18

Multi-objective genetic algorithm in solving conflicted goals for questions generating problem. / Suhaimi, Nur Suhailayani; Kamarudin, Siti Nur Kamaliah; Othman, Zalinda; Arbin, Norazam.

Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS. Vol. 2015-September IEEE Computer Society, 2015. p. 60-63 7280879.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Suhaimi, NS, Kamarudin, SNK, Othman, Z & Arbin, N 2015, Multi-objective genetic algorithm in solving conflicted goals for questions generating problem. in Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS. vol. 2015-September, 7280879, IEEE Computer Society, pp. 60-63, 5th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2014, Langkawi, Malaysia, 27/1/14. https://doi.org/10.1109/ISMS.2014.18
Suhaimi NS, Kamarudin SNK, Othman Z, Arbin N. Multi-objective genetic algorithm in solving conflicted goals for questions generating problem. In Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS. Vol. 2015-September. IEEE Computer Society. 2015. p. 60-63. 7280879 https://doi.org/10.1109/ISMS.2014.18
Suhaimi, Nur Suhailayani ; Kamarudin, Siti Nur Kamaliah ; Othman, Zalinda ; Arbin, Norazam. / Multi-objective genetic algorithm in solving conflicted goals for questions generating problem. Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS. Vol. 2015-September IEEE Computer Society, 2015. pp. 60-63
@inproceedings{438d12317141440e99afdb555fceeff5,
title = "Multi-objective genetic algorithm in solving conflicted goals for questions generating problem",
abstract = "Multi-objective genetic algorithm (MOGA) has been used for more than a decade to solve real-world optimization problems that have several, and often conflicting objectives. In this research, the conflicting objectives of achieving the maximum accuracy of the solution and at the same time minimizing the redundancy of the optimal solutions in retrieving the best set of exam questions for academicians for a particular subject are highlighted. Hence, the aim of this paper is to solve the multi-objective problem in a chromosome (solution) and also to maintain the fitness of the chromosome. The results of this research are measured based on the similarity achieved between the obtained and desired solutions. By using MOGA, a promising result is obtained with the maximum accuracy and simultaneously, minimizing the redundancy of the genes in a solution.",
keywords = "Genetic algorithm, MOGA, Multi-objectives",
author = "Suhaimi, {Nur Suhailayani} and Kamarudin, {Siti Nur Kamaliah} and Zalinda Othman and Norazam Arbin",
year = "2015",
month = "9",
day = "28",
doi = "10.1109/ISMS.2014.18",
language = "English",
isbn = "9781479938575",
volume = "2015-September",
pages = "60--63",
booktitle = "Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Multi-objective genetic algorithm in solving conflicted goals for questions generating problem

AU - Suhaimi, Nur Suhailayani

AU - Kamarudin, Siti Nur Kamaliah

AU - Othman, Zalinda

AU - Arbin, Norazam

PY - 2015/9/28

Y1 - 2015/9/28

N2 - Multi-objective genetic algorithm (MOGA) has been used for more than a decade to solve real-world optimization problems that have several, and often conflicting objectives. In this research, the conflicting objectives of achieving the maximum accuracy of the solution and at the same time minimizing the redundancy of the optimal solutions in retrieving the best set of exam questions for academicians for a particular subject are highlighted. Hence, the aim of this paper is to solve the multi-objective problem in a chromosome (solution) and also to maintain the fitness of the chromosome. The results of this research are measured based on the similarity achieved between the obtained and desired solutions. By using MOGA, a promising result is obtained with the maximum accuracy and simultaneously, minimizing the redundancy of the genes in a solution.

AB - Multi-objective genetic algorithm (MOGA) has been used for more than a decade to solve real-world optimization problems that have several, and often conflicting objectives. In this research, the conflicting objectives of achieving the maximum accuracy of the solution and at the same time minimizing the redundancy of the optimal solutions in retrieving the best set of exam questions for academicians for a particular subject are highlighted. Hence, the aim of this paper is to solve the multi-objective problem in a chromosome (solution) and also to maintain the fitness of the chromosome. The results of this research are measured based on the similarity achieved between the obtained and desired solutions. By using MOGA, a promising result is obtained with the maximum accuracy and simultaneously, minimizing the redundancy of the genes in a solution.

KW - Genetic algorithm

KW - MOGA

KW - Multi-objectives

UR - http://www.scopus.com/inward/record.url?scp=84959928665&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84959928665&partnerID=8YFLogxK

U2 - 10.1109/ISMS.2014.18

DO - 10.1109/ISMS.2014.18

M3 - Conference contribution

SN - 9781479938575

VL - 2015-September

SP - 60

EP - 63

BT - Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS

PB - IEEE Computer Society

ER -