{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/22817"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/22817","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Automated design of knowledge-lean heuristics: Learning, resource scheduling, and generalization","abstract":"Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T15:00:12Z Item is restricted indefinitely.","abstract_html":"Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T15:00:12Z Item is restricted indefinitely.","abstract_has_math":false,"creators":["Ieumwananonthachai, Arthur"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Wah, Benjamin W."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T13:52:27Z","date_published":"2011-05-07T13:52:27Z","updated_at":"2026-07-22T22:25:20Z","subjects":["Engineering, Electronics and Electrical","Artificial Intelligence","Computer Science"],"languages":["eng"],"rights":["Copyright 1996 Ieumwananonthachai, Arthur"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9780591087147","AAI9702544","(UMI)AAI9702544"],"render_values":[{"text":"9780591087147","href":null,"code":true},{"text":"AAI9702544","href":null,"code":true},{"text":"(UMI)AAI9702544","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/22817","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wah, Benjamin W."]},{"key":"dc:creator","label":"Author","values":["Ieumwananonthachai, Arthur"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T13:52:27Z","10000-01-01","1996"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Electronics and Electrical","Artificial Intelligence","Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1996 Ieumwananonthachai, Arthur"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["9780591087147","AAI9702544","(UMI)AAI9702544","http://hdl.handle.net/2142/22817"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T15:00:12Z Item is restricted indefinitely.","In this thesis we present new methods for the automated design of new heuristics in knowledge-lean applications and for finding heuristics that can be generalized to unlearned test cases. These applications lack domain knowledge for credit assignment; hence, operators for composing new heuristics are generally model free, domain independent, and syntactic in nature. The operators we have used are genetics based; examples of which include mutation and crossover. Learning is based on a generate-and-test paradigm that maintains a pool of competing heuristics, tests them to a limited extent, creates new ones from those that perform well in the past, and prunes poor ones from the pool. We have studied four important issues in learning better heuristics: (a) partitioning of a problem domain into smaller subsets, called subdomains, so that performance values within each subdomain can be evaluated statistically, (b) anomalies in performance evaluation within a subdomain, (c) rational scheduling of limited computational resources in testing candidate heuristics in single-objective as well as multi-objective learning, and (d) finding heuristics that can be generalized to unlearned sub domains.","We show experimental results in learning better heuristics for (a) process placement for distributed-memory multicomputers, (b) node decomposition in a branch-and-bound search, (c) generation of test patterns in VLSI circuit testing, (d) VLSI cell placement and routing, and (e) blind equalization.","Made available in DSpace on 2011-05-07T13:52:27Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9702544.pdf: 10563146 bytes, checksum: a1abac9a3c4c1b884c5da624409e3d59 (MD5) Previous issue date: 1996","Restriction data tranferred 2014-07-01T11:28:28-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["Automated design of knowledge-lean heuristics: Learning, resource scheduling, and generalization"]}]}],"canonical_facts":{"dc:contributor":["Wah, Benjamin W."],"dc:creator":["Ieumwananonthachai, Arthur"],"dc:date":["2011-05-07T13:52:27Z","10000-01-01","1996"],"dc:description":["Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T15:00:12Z Item is restricted indefinitely.","In this thesis we present new methods for the automated design of new heuristics in knowledge-lean applications and for finding heuristics that can be generalized to unlearned test cases. These applications lack domain knowledge for credit assignment; hence, operators for composing new heuristics are generally model free, domain independent, and syntactic in nature. The operators we have used are genetics based; examples of which include mutation and crossover. Learning is based on a generate-and-test paradigm that maintains a pool of competing heuristics, tests them to a limited extent, creates new ones from those that perform well in the past, and prunes poor ones from the pool. We have studied four important issues in learning better heuristics: (a) partitioning of a problem domain into smaller subsets, called subdomains, so that performance values within each subdomain can be evaluated statistically, (b) anomalies in performance evaluation within a subdomain, (c) rational scheduling of limited computational resources in testing candidate heuristics in single-objective as well as multi-objective learning, and (d) finding heuristics that can be generalized to unlearned sub domains.","We show experimental results in learning better heuristics for (a) process placement for distributed-memory multicomputers, (b) node decomposition in a branch-and-bound search, (c) generation of test patterns in VLSI circuit testing, (d) VLSI cell placement and routing, and (e) blind equalization.","Made available in DSpace on 2011-05-07T13:52:27Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9702544.pdf: 10563146 bytes, checksum: a1abac9a3c4c1b884c5da624409e3d59 (MD5) Previous issue date: 1996","Restriction data tranferred 2014-07-01T11:28:28-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"],"dc:identifier":["9780591087147","AAI9702544","(UMI)AAI9702544","http://hdl.handle.net/2142/22817"],"dc:language":["eng"],"dc:rights":["Copyright 1996 Ieumwananonthachai, Arthur"],"dc:subject":["Engineering, Electronics and Electrical","Artificial Intelligence","Computer Science"],"dc:title":["Automated design of knowledge-lean heuristics: Learning, resource scheduling, and generalization"],"dc:type":["text"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:20Z"}