Performance characteristics of alternative genetic algorithmic approaches to the traveling salesman problem using path representation: An empirical study |
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| Institution: | 1. Department of Information Technology Management, Christian Brothers University, 650 East Parkway South, Memphis, TN 38104, USA;2. Department of Management Information Systems and Decision Sciences, Fogelman College of Business and Economics, The University of Memphis, Memphis, TN 38152, USA;1. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain;2. Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain;3. Pompeu Fabra University, Barcelona, Spain;4. BioMedical Center (BMC), Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany;5. German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany;6. Alzheimer''s Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d''Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain;7. Unit of human Anatomy and Embryology, Department of Morphological Sciences, Faculty of Medicine, Autonomous University of Barcelona, Cerdanyola del Vallès, Spain;8. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany;1. School of Engineering and ICT, University of Tasmania, Private Bag 65, Sandy Bay, Tasmania 7001, Australia;2. Discipline of Geography and Spatial Sciences, School of Land and Food, University of Tasmania, Private Bag 76, Sandy Bay, Tasmania 7001, Australia;3. School of Physical Sciences, University of Tasmania, Private Bag 37, Sandy Bay, Tasmania 7001, Australia;4. Forestry Tasmania, 79 Melville St, Hobart, TAS 7000, Australia |
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| Abstract: | The potential of Genetic Algorithmic (GA) approaches for solving order-based problems including the Traveling Salesman Problem (TSP) is recognized in a number of recent studies. By applying various GAs, these studies developed a set of unresolved GA design and configuration issues. The purpose of this study is to resolve the conflicting GA design and configuration issues by (1) concentrating on the classical TSP; and (2) developing, implementing, and testing a complete set of alternative GA configurations, 144 GAs are developed and evaluated by solvinh 5000 TSPs. A carefully designed statistical experimental plan accompanied by rigorous statistical analysis isolate the most promising configurations and identify their effect on solution time and quality. Although, the emphasis is on the TSP, the final results are applicable to other order-based problems that use sequence encoding. |
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