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<rdf:Description rdf:about="http://weko.wou.edu.my/?action=repository_uri&amp;item_id=498">
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	<swrc:title xml:lang="en">Research review: a modified micro genetic algorithm for undertaking multi-objective optimization problems</swrc:title>
	<dc:language>en</dc:language>
	<swrc:keyword xml:lang="ja">Multi-objective optimization</swrc:keyword>
	<swrc:keyword xml:lang="ja">Micro genetic algorithm</swrc:keyword>
	<swrc:editor xml:lang="en">Choo Jun Tan</swrc:editor>
	<swrc:abstract xml:lang="en">A Modified micro Genetic Algorithm (MmGA) is proposed for undertaking Multi-objective Optimization Problems (MOPs). An NSGA-II inspired elitism strategy and a population initialization strategy are embedded into the traditional micro Genetic Algorithm (mGA) to form the proposed MmGA. The main aim of the MmGA is to improve its convergence rate towards the pareto optimal solutions. To evaluate the effectiveness of the MmGA, two experiments using the Kursawe test function in MOPs are conducted, and the results are compared with those from other approaches using a multi-objective evolutionary algorithm indicator, i.e. the Generational Distance (GD). The outcomes positively demonstrate that the MmGA is able to provide useful solutions with improved GD measures for tackling MOPs.</swrc:abstract>
	<swrc:type xml:lang="en"> Electronic </swrc:type>
	<dc:contributor xml:lang="en">Wawasan Open University</dc:contributor>
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