{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/27027"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/27027","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"A Convergence Analysis of Generalized Hill Climbing Algorithms","abstract":"Generalized hill climbing (GHC) algorithms provide a unifying framework for describing several discrete optimization problem local search heuristics, including simulated annealing and tabu search. A necessary and a sufficient convergence condition for GHC algorithms are presented. The convergence conditions presented in this dissertation are based upon a new iteration classification scheme for GHC algorithms. The convergence theory for particular formulations of GHC algorithms is presented and the implications discussed. Examples are provided to illustrate the relationship between the new convergence conditions and previously existing convergence conditions in the literature. The contributions of the necessary and the sufficient convergence conditions for GHC algorithms are discussed and future research endeavors are suggested.","abstract_html":"Generalized hill climbing (GHC) algorithms provide a unifying framework for describing several discrete optimization problem local search heuristics, including simulated annealing and tabu search. A necessary and a sufficient convergence condition for GHC algorithms are presented. The convergence conditions presented in this dissertation are based upon a new iteration classification scheme for GHC algorithms. The convergence theory for particular formulations of GHC algorithms is presented and the implications discussed. Examples are provided to illustrate the relationship between the new convergence conditions and previously existing convergence conditions in the literature. The contributions of the necessary and the sufficient convergence conditions for GHC algorithms are discussed and future research endeavors are suggested.","abstract_has_math":false,"creators":["Sullivan, Kelly Ann"],"institution":"Virginia Tech","degree_name":"Ph. D.","degree_level":"doctoral","degree_discipline":"Industrial and Systems Engineering","degree_department":"Industrial and Systems Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Jacobson, Sheldon H."],"committee_members":["Nachlas, Joel A.","Ye, Keying","Sherali, Hanif D.","Kobza, John E."],"year":1999,"date_issued":"1999-04-12","date_published":"1999-04-12","updated_at":"2026-07-22T22:19:27Z","subjects":["heuristics","discrete optimization","local search","simulated annealing","convergence","combinatorial optimization","hill climbing algorithms","Markov chain"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-041999-213806"],"render_values":[{"text":"etd-041999-213806","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/27027","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Jacobson, Sheldon H."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Nachlas, Joel A.","Ye, Keying","Sherali, Hanif D.","Kobza, John E."]},{"key":"dc:contributor.department","label":"Department","values":["Industrial and Systems Engineering"]},{"key":"dc:creator","label":"Author","values":["Sullivan, Kelly Ann"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:10:08Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:10:08Z","1999-04-21"]},{"key":"dc:date.issued","label":"Date","values":["1999-04-12"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["heuristics","discrete optimization","local search","simulated annealing","convergence","combinatorial optimization","hill climbing algorithms","Markov chain"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-041999-213806"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/27027"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Generalized hill climbing (GHC) algorithms provide a unifying framework for describing several discrete optimization problem local search heuristics, including simulated annealing and tabu search. A necessary and a sufficient convergence condition for GHC algorithms are presented. The convergence conditions presented in this dissertation are based upon a new iteration classification scheme for GHC algorithms. The convergence theory for particular formulations of GHC algorithms is presented and the implications discussed. Examples are provided to illustrate the relationship between the new convergence conditions and previously existing convergence conditions in the literature. The contributions of the necessary and the sufficient convergence conditions for GHC algorithms are discussed and future research endeavors are suggested."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph. D."]},{"key":"dc:title","label":"Title","values":["A Convergence Analysis of Generalized Hill Climbing Algorithms"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Jacobson, Sheldon H."],"dc:contributor.committeemember":["Nachlas, Joel A.","Ye, Keying","Sherali, Hanif D.","Kobza, John E."],"dc:contributor.department":["Industrial and Systems Engineering"],"dc:creator":["Sullivan, Kelly Ann"],"dc:date.accessioned":["2014-03-14T20:10:08Z"],"dc:date.available":["2014-03-14T20:10:08Z","1999-04-21"],"dc:date.issued":["1999-04-12"],"dc:description.abstract":["Generalized hill climbing (GHC) algorithms provide a unifying framework for describing several discrete optimization problem local search heuristics, including simulated annealing and tabu search. A necessary and a sufficient convergence condition for GHC algorithms are presented. The convergence conditions presented in this dissertation are based upon a new iteration classification scheme for GHC algorithms. The convergence theory for particular formulations of GHC algorithms is presented and the implications discussed. Examples are provided to illustrate the relationship between the new convergence conditions and previously existing convergence conditions in the literature. The contributions of the necessary and the sufficient convergence conditions for GHC algorithms are discussed and future research endeavors are suggested."],"dc:description.degree":["Ph. D."],"dc:identifier.other":["etd-041999-213806"],"dc:identifier.uri":["http://hdl.handle.net/10919/27027"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["heuristics","discrete optimization","local search","simulated annealing","convergence","combinatorial optimization","hill climbing algorithms","Markov chain"],"dc:title":["A Convergence Analysis of Generalized Hill Climbing Algorithms"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Industrial and Systems Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Ph. D."],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:27Z"}