{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/21761"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/21761","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Robust electronic circuit design using evolutionary and Taguchi methods","abstract":"In engineering, there is a wide range of applications where genetic optimizers are used. Two genetic optimizers used in this thesis namely, Population Based Incremental Learning ( PBIL ) and Cross generational selection Heterogeneous crossover Cataclysmic mutation ( CHC ), are tested on a series of circuit problems to fmd if robust electronic circuits can be built from evolutionary methods. The evolutionary algorithms were used to search the space of discrete component values from a range of manufactured preferred values to obtain robust electronic circuits. Parasitic effects were also modelled in the simulation to provide for a more realistic circuit.","abstract_html":"In engineering, there is a wide range of applications where genetic optimizers are used. Two genetic optimizers used in this thesis namely, Population Based Incremental Learning ( PBIL ) and Cross generational selection Heterogeneous crossover Cataclysmic mutation ( CHC ), are tested on a series of circuit problems to fmd if robust electronic circuits can be built from evolutionary methods. The evolutionary algorithms were used to search the space of discrete component values from a range of manufactured preferred values to obtain robust electronic circuits. Parasitic effects were also modelled in the simulation to provide for a more realistic circuit.","abstract_has_math":false,"creators":["Owadally, Muhammud Asaad"],"institution":"Department of Electrical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Greene, John R"],"committee_chairs":[],"committee_members":[],"year":1997,"date_issued":"1997","date_published":"1997","updated_at":"2026-07-22T22:23:17Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/21761","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Greene, John R"]},{"key":"dc:creator","label":"Author","values":["Owadally, Muhammud Asaad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-09-14T12:56:19Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-09-14T12:56:19Z"]},{"key":"dc:date.issued","label":"Date","values":["1997"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electrical Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/21761"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Bibliography: pages 80-81."]},{"key":"dc:description.abstract","label":"Abstract","values":["In engineering, there is a wide range of applications where genetic optimizers are used. Two genetic optimizers used in this thesis namely, Population Based Incremental Learning ( PBIL ) and Cross generational selection Heterogeneous crossover Cataclysmic mutation ( CHC ), are tested on a series of circuit problems to fmd if robust electronic circuits can be built from evolutionary methods. The evolutionary algorithms were used to search the space of discrete component values from a range of manufactured preferred values to obtain robust electronic circuits. Parasitic effects were also modelled in the simulation to provide for a more realistic circuit."]},{"key":"dc:title","label":"Title","values":["Robust electronic circuit design using evolutionary and Taguchi methods"]}]}],"canonical_facts":{"dc:contributor.advisor":["Greene, John R"],"dc:creator":["Owadally, Muhammud Asaad"],"dc:date.accessioned":["2016-09-14T12:56:19Z"],"dc:date.available":["2016-09-14T12:56:19Z"],"dc:date.issued":["1997"],"dc:description":["Bibliography: pages 80-81."],"dc:description.abstract":["In engineering, there is a wide range of applications where genetic optimizers are used. Two genetic optimizers used in this thesis namely, Population Based Incremental Learning ( PBIL ) and Cross generational selection Heterogeneous crossover Cataclysmic mutation ( CHC ), are tested on a series of circuit problems to fmd if robust electronic circuits can be built from evolutionary methods. 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