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A Transdisciplinary Approach to Enhancing Online Engineering Education Through Learning Analytics Masikini Lugoma , Lethuxolo Yende , Pule Dikgwatlhe , Akhona Mkonde , Rorisang Thage , Lucky Maseko , Ngonidzashe Chimwani (pages: 1-6) AI Disruptions in Higher Education: Evolutionary Change, Not Revolutionary Overthrow Cristo Leon , James Lipuma , Maximus Rafla (pages: 7-18) Education, Research, and Methodology: A Transdisciplinary Cybernetic Whole Nagib Callaos , Cristo Leon (pages: 19-33) Enhancing Educational Effectiveness Through Transdisciplinary Practice: The ETCOP Model Birgit Oberer , Alptekin Erkollar , Andreas Kropfberger (pages: 34-40) From Instruction to Interaction: Reflexive Learning Design for Cross-Generational Engagement at the Workplace Gita Aulia Nurani , Ya-Hui Lee (pages: 41-44) GIS in Aquatic Animal Health Surveillance: A Transdisciplinary eLearning Initiative Integrating Education, Research, and Methodology (The Aquae Strength Project) Eleonora Franzago , Rodrigo Macario , Matteo Mazzucato , Federica Sbettega , Manuela Cassani , Guido Ricaldi , Francesco Bissoli , Anna Nadin , Fabrizio Personeni , Manuela Dalla Pozza , Grazia Manca , Nicola Ferré (pages: 45-50) Reflexivity as a Compass: The European AI Act and Its Implications for U.S. Higher Education Institutions Jasmin Cowin (pages: 51-56) Required General Education Program Evaluation: Bridging the Gap Between Educators and Administrators James Lipuma , Cristo Leon , Jeremy Reich (pages: 57-61) Researching Ourselves Jeremy Horne (pages: 62-72) The Self-Aware, Reflective Learner: Fostering Metacognitive Awareness and Reflexivity in Undergraduates Through Service-Learning Genejane Adarlo (pages: 73-81)
ABSTRACT
Parameter Selection in Genetic Algorithms
In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each parameter are proposed for a particular problem domain. Controversial to existing literature on GA, our computational results reveal that in the case of a dominant set of decision variable the crossover operator does not have a significant impact on the performance measures, whereas high mutation rates are more suitable for GA applications.
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