COURSE SCHEDULE (Weekly) WEEK TOPICS 1 Introduction to Genetic and Evolutionary Algorithms 2 Classic Optimization Problems 3 Classic Genetic Algorithms 4 Mathematical Models of Genetic Algorithms 5 Evolutionary Programming 6 Midterm Examination 1 7 Evolutionary Strategies 8 Genetic Programming 9 Types of Evolutionary and Genetic Algorithms I 10 Types of Evolutionary and Genetic Algorithms II 11 Midterm Examination 2 12 Combinatorial Optimization 13 Restricted Optimization 14 Multiobjective Optimization 15,16 Final Examination CONTRIBUTION OF THE COURSE LEARNING OUTCOMES TO THE CONTRIBUTION INDUSTRIAL ENGINEERING PhD PROGRAM LEARNING OUTCOMES LEVEL 1 3 2 NO LEARNING OUTCOMES (PhD) Low High Mid Ability to understand and implement mathematics, basic and LO 1 engineering sciences at utmost level in the field of Industrial Engineering and other relevant fields. Ability to the newest knowledge, design, plan, manage, finalize and implement original research processes bringing innovation to LO 2 science or technology in the field of Industrial Engineering and other relevant fields. Ability to design, plan, manage, finalize and implement LO 3 multidisciplinary innovative studies Ability to present and publish the results of academic studies at all LO 4 kind of platforms. Ability to use at least one language sufficiently, skills for written, LO 5 verbal, visual communication and discussion in that language. Ability to make evaluation, critical analysis and synthesis about LO 6 conceptions that are generated in the relevant field. Ability to evaluate actual scientific, technological, social, cultural and LO 7 environmental developments besides awareness of scientific neutrality, ethics and responsibility. Prepared by : Prof.Dr. Muzaffer KAPANOĞLU Date: 03.11.2015 Signature: INDUSTRIAL ENGINEERING (MSc) DEPARTMENT SEMESTER COURSE CODE 503201510 TITLE Decision Support Systems and Expert Systems
COURSE SCHEDULE (Weekly) WEEK TOPICS 1 Introduction to Genetic and Evolutionary Algorithms 2 Classic Optimization Problems 3 Classic Genetic Algorithms 4 Mathematical Models of Genetic Algorithms 5 Evolutionary Programming 6 Midterm Examination 1 7 Evolutionary Strategies 8 Genetic Programming 9 Types of Evolutionary and Genetic Algorithms I 10 Types of Evolutionary and Genetic Algorithms II 11 Midterm Examination 2 12 Combinatorial Optimization 13 Restricted Optimization 14 Multiobjective Optimization 15,16 Final Examination CONTRIBUTION OF THE COURSE LEARNING OUTCOMES TO THE INDUSTRIAL ENGINEERING PhD PROGRAM LEARNING OUTCOMES CONTRIBUTION LEVEL NO LEARNING OUTCOMES (PhD) 3 High 2 Mid 1 Low LO 1 Ability to understand and implement mathematics, basic and engineering sciences at utmost level in the field of Industrial Engineering and other relevant fields. LO 2 Ability to the newest knowledge, design, plan, manage, finalize and implement original research processes bringing innovation to science or technology in the field of Industrial Engineering and other relevant fields. LO 3 Ability to design, plan, manage, finalize and implement multidisciplinary innovative studies LO 4 Ability to present and publish the results of academic studies at all kind of platforms. LO 5 Ability to use at least one language sufficiently, skills for written, verbal, visual communication and discussion in that language. LO 6 Ability to make evaluation, critical analysis and synthesis about conceptions that are generated in the relevant field. LO 7 Ability to evaluate actual scientific, technological, social, cultural and environmental developments besides awareness of scientific neutrality, ethics and responsibility. Prepared by : Prof.Dr. Muzaffer KAPANOĞLU Date: 03.11.2015 Signature: DEPARTMENT INDUSTRIAL ENGINEERING (MSc) SEMESTER COURSE CODE 503201510 TITLE Decision Support Systems and Expert Systems
Information%20Package%20and%20Course%20Catalogue.docx