{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1828"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1828","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"A targeted reverse mapping machine learning approach for non-dominated solutions in multi-objective optimization","abstract":"Multi-objective optimization problems aim to identify solutions that maximize or minimize conflicting objectives. Population-based multi-objective algorithms, inspired by biological populations, are effective but often provide limited solutions within the decisionmakers’ region of interest (ROI) on the Pareto front. Recent advancements in machine learning have shown promise in generating solutions, yet they suffer from a lack of control and require knowledge of objective function attributes. This study proposes a framework using Gaussian process regression and artificial neural networks to generate innovative solutions in the ROI. By employing diverse sampling techniques and integrating long term memory, the framework can produce more than twice as many solutions in the ROI, as demonstrated in experiments with real-world problems and various benchmark functions.","abstract_html":"Multi-objective optimization problems aim to identify solutions that maximize or minimize conflicting objectives. Population-based multi-objective algorithms, inspired by biological populations, are effective but often provide limited solutions within the decisionmakers’ region of interest (ROI) on the Pareto front. Recent advancements in machine learning have shown promise in generating solutions, yet they suffer from a lack of control and require knowledge of objective function attributes. This study proposes a framework using Gaussian process regression and artificial neural networks to generate innovative solutions in the ROI. By employing diverse sampling techniques and integrating long term memory, the framework can produce more than twice as many solutions in the ROI, as demonstrated in experiments with real-world problems and various benchmark functions.","abstract_has_math":false,"creators":["Kermani Poor, Masoud"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Ebrahimi, Mehran","Rahnamayan, Shahryar"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-08-01","date_published":"2024-08-01","updated_at":"2026-07-24T05:35:34Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1828","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ebrahimi, Mehran","Rahnamayan, Shahryar"]},{"key":"dc:creator","label":"Author","values":["Kermani Poor, Masoud"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-08-27T17:36:38Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-08-27T17:36:38Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-08-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1828"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Multi-objective optimization problems aim to identify solutions that maximize or minimize conflicting objectives. Population-based multi-objective algorithms, inspired by biological populations, are effective but often provide limited solutions within the decisionmakers’ region of interest (ROI) on the Pareto front. Recent advancements in machine learning have shown promise in generating solutions, yet they suffer from a lack of control and require knowledge of objective function attributes. This study proposes a framework using Gaussian process regression and artificial neural networks to generate innovative solutions in the ROI. By employing diverse sampling techniques and integrating long term memory, the framework can produce more than twice as many solutions in the ROI, as demonstrated in experiments with real-world problems and various benchmark functions."]},{"key":"dc:title","label":"Title","values":["A targeted reverse mapping machine learning approach for non-dominated solutions in multi-objective optimization"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ebrahimi, Mehran","Rahnamayan, Shahryar"],"dc:creator":["Kermani Poor, Masoud"],"dc:date.accessioned":["2024-08-27T17:36:38Z"],"dc:date.available":["2024-08-27T17:36:38Z"],"dc:date.issued":["2024-08-01"],"dc:description.abstract":["Multi-objective optimization problems aim to identify solutions that maximize or minimize conflicting objectives. Population-based multi-objective algorithms, inspired by biological populations, are effective but often provide limited solutions within the decisionmakers’ region of interest (ROI) on the Pareto front. Recent advancements in machine learning have shown promise in generating solutions, yet they suffer from a lack of control and require knowledge of objective function attributes. This study proposes a framework using Gaussian process regression and artificial neural networks to generate innovative solutions in the ROI. By employing diverse sampling techniques and integrating long term memory, the framework can produce more than twice as many solutions in the ROI, as demonstrated in experiments with real-world problems and various benchmark functions."],"dc:identifier.uri":["https://hdl.handle.net/10155/1828"],"dc:language.iso":["en"],"dc:title":["A targeted reverse mapping machine learning approach for non-dominated solutions in multi-objective optimization"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:34Z"}