The present status of the field of Machine Learning (ML) focuses on optimization of popular models. Rarely are the effects of the problem characteristics upon the solution algorithm studied. There exists no standard for knowing when to apply ML algorithms to a given problem or how to estimate the effectiveness of results. Focusing on the search space of problems, a rigorous study was conducted to generate an in-depth understanding of the impact of search space characteristics to the performance of a ML algorithm, specifically a Genetic Algorithm (GA). The effects of specific problem characteristics, represented via solution space characteristics, on the efficiency and solution quality of a GA were measured. The results allow researchers to adapt ML techniques and estimate quality of results provided the solution space characteristics can be determined and mapped to those in this study. As part of this study standardized terms for solution space characteristics were developed. Standardization of these terms, used without formal definition in the literature, allow for a uniform characterization of search spaces, which in turn allows the results of this study to be transferred to other domains.
","abstract_html":"<p>The present status of the field of Machine Learning (ML) focuses on optimization of popular models. Rarely are the effects of the problem characteristics upon the solution algorithm studied. There exists no standard for knowing when to apply ML algorithms to a given problem or how to estimate the effectiveness of results. Focusing on the search space of problems, a rigorous study was conducted to generate an in-depth understanding of the impact of search space characteristics to the performance of a ML algorithm, specifically a Genetic Algorithm (GA). The effects of specific problem characteristics, represented via solution space characteristics, on the efficiency and solution quality of a GA were measured. The results allow researchers to adapt ML techniques and estimate quality of results provided the solution space characteristics can be determined and mapped to those in this study. As part of this study standardized terms for solution space characteristics were developed. Standardization of these terms, used without formal definition in the literature, allow for a uniform characterization of search spaces, which in turn allows the results of this study to be transferred to other domains.</p>","abstract_has_math":false,"creators":["Ghelarducci, Leo"],"institution":null,"degree_name":"Doctor of Philosophy in Electrical Engineering & Computer Science","degree_level":"Dissertation - Open Access","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-01T07:00:00Z","date_published":"2025-08-01T07:00:00Z","updated_at":"2026-07-27T19:26:22Z","subjects":["Solution Spaces","Landscapes","Machine Learning","Artificial Intelligence","Problems","Artificial Intelligence and Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/925","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Ghelarducci, Leo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Electrical Engineering & Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Solution Spaces","Landscapes","Machine Learning","Artificial Intelligence","Problems","Artificial Intelligence and Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/925"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The present status of the field of Machine Learning (ML) focuses on optimization of popular models. Rarely are the effects of the problem characteristics upon the solution algorithm studied. There exists no standard for knowing when to apply ML algorithms to a given problem or how to estimate the effectiveness of results. Focusing on the search space of problems, a rigorous study was conducted to generate an in-depth understanding of the impact of search space characteristics to the performance of a ML algorithm, specifically a Genetic Algorithm (GA). The effects of specific problem characteristics, represented via solution space characteristics, on the efficiency and solution quality of a GA were measured. The results allow researchers to adapt ML techniques and estimate quality of results provided the solution space characteristics can be determined and mapped to those in this study. As part of this study standardized terms for solution space characteristics were developed. Standardization of these terms, used without formal definition in the literature, allow for a uniform characterization of search spaces, which in turn allows the results of this study to be transferred to other domains.
"]},{"key":"dc:title","label":"Title","values":["Characterization of Search Spaces and Effects on Machine Learning"]}]}],"canonical_facts":{"dc:creator":["Ghelarducci, Leo"],"dc:description.abstract":["The present status of the field of Machine Learning (ML) focuses on optimization of popular models. Rarely are the effects of the problem characteristics upon the solution algorithm studied. There exists no standard for knowing when to apply ML algorithms to a given problem or how to estimate the effectiveness of results. Focusing on the search space of problems, a rigorous study was conducted to generate an in-depth understanding of the impact of search space characteristics to the performance of a ML algorithm, specifically a Genetic Algorithm (GA). The effects of specific problem characteristics, represented via solution space characteristics, on the efficiency and solution quality of a GA were measured. The results allow researchers to adapt ML techniques and estimate quality of results provided the solution space characteristics can be determined and mapped to those in this study. As part of this study standardized terms for solution space characteristics were developed. Standardization of these terms, used without formal definition in the literature, allow for a uniform characterization of search spaces, which in turn allows the results of this study to be transferred to other domains.
"],"dc:identifier":["https://commons.erau.edu/edt/925"],"dc:subject":["Solution Spaces","Landscapes","Machine Learning","Artificial Intelligence","Problems","Artificial Intelligence and Robotics"],"dc:title":["Characterization of Search Spaces and Effects on Machine Learning"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy in Electrical Engineering & Computer Science"]},"updated_at":"2026-07-27T19:26:22Z"}