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class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Sadat Hasnayen</h3></div><div class="js-work-strip profile--work_container" data-work-id="65439984"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/65439984/Multiobjective_Optimization_Using_Genetic_Algorithm"><img alt="Research paper thumbnail of Multiobjective Optimization Using Genetic Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/77036057/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/65439984/Multiobjective_Optimization_Using_Genetic_Algorithm">Multiobjective Optimization Using Genetic Algorithm</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In case of Multi-objective optimization problems (MOP), objective vector can be scalarized into a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In case of Multi-objective optimization problems (MOP), objective vector can be scalarized into a single objective and the yielded objective is highly sensitive to the objective weight vectors and it asks the user to have knowledge about the underlying problems. Moreover in the case of Multi-objective optimization problems, one may require a set of Pareto-Optimal points in the search space, instead of a single point. Since Genetic Algorithm (GA) works with a set of individual solutions called population, it is natural to adopt GA schemes for Multi-objective Optimization problems so that one can capture a number of solutions simultaneously. Although many techniques have been developed to solve these types of problems, namely VEGA, MOGA, NPGA, NSGA etc, all of them have some shortcomings. This project proposal explains a new approach to solve these types of problems by subdividing the population with respect to each overlapping pair of objective functions and their merging through gen...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b7471d0798b666719a7fd850531d9efc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":77036057,"asset_id":65439984,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/77036057/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="65439984"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="65439984"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65439984; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="13398663" id="papers"><div class="js-work-strip profile--work_container" data-work-id="65439984"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/65439984/Multiobjective_Optimization_Using_Genetic_Algorithm"><img alt="Research paper thumbnail of Multiobjective Optimization Using Genetic Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/77036057/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/65439984/Multiobjective_Optimization_Using_Genetic_Algorithm">Multiobjective Optimization Using Genetic Algorithm</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In case of Multi-objective optimization problems (MOP), objective vector can be scalarized into a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In case of Multi-objective optimization problems (MOP), objective vector can be scalarized into a single objective and the yielded objective is highly sensitive to the objective weight vectors and it asks the user to have knowledge about the underlying problems. Moreover in the case of Multi-objective optimization problems, one may require a set of Pareto-Optimal points in the search space, instead of a single point. Since Genetic Algorithm (GA) works with a set of individual solutions called population, it is natural to adopt GA schemes for Multi-objective Optimization problems so that one can capture a number of solutions simultaneously. Although many techniques have been developed to solve these types of problems, namely VEGA, MOGA, NPGA, NSGA etc, all of them have some shortcomings. This project proposal explains a new approach to solve these types of problems by subdividing the population with respect to each overlapping pair of objective functions and their merging through gen...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b7471d0798b666719a7fd850531d9efc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":77036057,"asset_id":65439984,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/77036057/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="65439984"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="65439984"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65439984; 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