This thesis proposes and investigates a new hybrid technique based on Genetic Programming (GP) and Ant Colony Optimization (ACO) techniques for inducing data classification rules. The proposed hybrid approach aims to improve on the accuracy of data classification rules produced by the original GP technique, which uses randomly generated initial populations. This hybrid technique relies on the ACO technique to produce the initial populations for the GP technique. To evaluate and compare their effectiveness in producing good data classification rules, GP, ACO, and hybrid techniques were implemented in the C programming language. The data classification rules were created and evaluated by executing these codes with two datasets for credit scoring problems, widely known as the Australian and German datasets, available from the Machine Learning Repository at the University of California, Irvine. The experimental results demonstrate that although all tree techniques yield similar accuracy during testing, on average, the hybrid ACO-GP approach performs better than either GP or ACO during training.
","abstract_html":"<p>This thesis proposes and investigates a new hybrid technique based on Genetic Programming (GP) and Ant Colony Optimization (ACO) techniques for inducing data classification rules. The proposed hybrid approach aims to improve on the accuracy of data classification rules produced by the original GP technique, which uses randomly generated initial populations. This hybrid technique relies on the ACO technique to produce the initial populations for the GP technique. To evaluate and compare their effectiveness in producing good data classification rules, GP, ACO, and hybrid techniques were implemented in the C programming language. The data classification rules were created and evaluated by executing these codes with two datasets for credit scoring problems, widely known as the Australian and German datasets, available from the Machine Learning Repository at the University of California, Irvine. The experimental results demonstrate that although all tree techniques yield similar accuracy during testing, on average, the hybrid ACO-GP approach performs better than either GP or ACO during training.</p>","abstract_has_math":false,"creators":["Aliehyaei, Rojin"],"institution":null,"degree_name":"Computer Science - Applied Computing Track","degree_level":"Thesis","degree_discipline":"TSYS School of Computer Science","degree_department":null,"school":null,"contributors":["Dr. Shamim Kahn","Dr. Timothy G. Howard","Dr. Lydia Ray"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-01-01T08:00:00Z","date_published":"2012-01-01T08:00:00Z","updated_at":"2026-07-24T01:44:55Z","subjects":["Genetic Programming (GP)","Ant Colony Optimization","C Programming Language (ACO)","Data Classification Rules","Machine Learning Repository","Computer Sciences","Databases and Information Systems"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://csuepress.columbusstate.edu/theses_dissertations/61","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Shamim Kahn","Dr. Timothy G. Howard","Dr. Lydia Ray"]},{"key":"dc:creator","label":"Author","values":["Aliehyaei, Rojin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-10-06T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["TSYS School of Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Computer Science - Applied Computing Track"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Genetic Programming (GP)","Ant Colony Optimization","C Programming Language (ACO)","Data Classification Rules","Machine Learning Repository","Computer Sciences","Databases and Information Systems"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://csuepress.columbusstate.edu/theses_dissertations/61"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis proposes and investigates a new hybrid technique based on Genetic Programming (GP) and Ant Colony Optimization (ACO) techniques for inducing data classification rules. The proposed hybrid approach aims to improve on the accuracy of data classification rules produced by the original GP technique, which uses randomly generated initial populations. This hybrid technique relies on the ACO technique to produce the initial populations for the GP technique. To evaluate and compare their effectiveness in producing good data classification rules, GP, ACO, and hybrid techniques were implemented in the C programming language. The data classification rules were created and evaluated by executing these codes with two datasets for credit scoring problems, widely known as the Australian and German datasets, available from the Machine Learning Repository at the University of California, Irvine. The experimental results demonstrate that although all tree techniques yield similar accuracy during testing, on average, the hybrid ACO-GP approach performs better than either GP or ACO during training.
"]},{"key":"dc:title","label":"Title","values":["An Investigation Into a Hybrid Genetic Programming and Ant Colony Optimization Method for Credit Scoring"]}]}],"canonical_facts":{"dc:contributor":["Dr. Shamim Kahn","Dr. Timothy G. Howard","Dr. Lydia Ray"],"dc:creator":["Aliehyaei, Rojin"],"dc:date.available":["2015-10-06T07:00:00Z"],"dc:description.abstract":["This thesis proposes and investigates a new hybrid technique based on Genetic Programming (GP) and Ant Colony Optimization (ACO) techniques for inducing data classification rules. The proposed hybrid approach aims to improve on the accuracy of data classification rules produced by the original GP technique, which uses randomly generated initial populations. This hybrid technique relies on the ACO technique to produce the initial populations for the GP technique. To evaluate and compare their effectiveness in producing good data classification rules, GP, ACO, and hybrid techniques were implemented in the C programming language. The data classification rules were created and evaluated by executing these codes with two datasets for credit scoring problems, widely known as the Australian and German datasets, available from the Machine Learning Repository at the University of California, Irvine. The experimental results demonstrate that although all tree techniques yield similar accuracy during testing, on average, the hybrid ACO-GP approach performs better than either GP or ACO during training.
"],"dc:identifier":["https://csuepress.columbusstate.edu/theses_dissertations/61"],"dc:language":["English"],"dc:subject":["Genetic Programming (GP)","Ant Colony Optimization","C Programming Language (ACO)","Data Classification Rules","Machine Learning Repository","Computer Sciences","Databases and Information Systems"],"dc:title":["An Investigation Into a Hybrid Genetic Programming and Ant Colony Optimization Method for Credit Scoring"],"thesis:degree_discipline":["TSYS School of Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Computer Science - Applied Computing Track"]},"updated_at":"2026-07-24T01:44:55Z"}