A multi-objective genetic algorithm for solving conflicted goals in questions generating problems

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

Research output: Contribution to journalArticle

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
Pages (from-to)30-34
Number of pages5
JournalInternational Journal of Simulation: Systems, Science and Technology
Volume15
Issue number3
DOIs
Publication statusPublished - 2014

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

Keywords

  • Component
  • Genetic algorithms
  • MOGA
  • Multi-objective

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation

Cite this

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

In: International Journal of Simulation: Systems, Science and Technology, Vol. 15, No. 3, 2014, p. 30-34.

Research output: Contribution to journalArticle

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