Especially in recent years, it has been highly observed that nature-based evolutionary algorithms are used extensively in optimization approaches in problem solving. In this course, the use of algorithms with different evolution such as Genetic Algorithm, Ant Colony Algorithm, Particle Swarm Optimization, Simulated Annealing, Bee Colony will be shown through examples and how the related algorithms will be used in different application areas will be emphasized. This course will be especially useful for students who want to study in this field.
Prerequisite(s)
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Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
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Not offered.
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Not offered.
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Program Outcomes
PO-1
an ability to apply knowledge from undergraduate and graduate engineering and other disciplines to identify, formulate, and solve novel and complex electrical/computer engineering problems that require advanced knowledge within the field
PO-2
knowledge of advanced topics within at least two subdisciplines of computer engineering
PO-3
the ability to understand and integrate new knowledge within the field;
PO-4
the ability to apply advanced technical knowledge in multiple contexts
PO-5
a recognition of the need for, and an ability to engage in, life-long learning
PO-6
the ability to plan and conduct an organized and systematic study on a significant topic within the field
PO-7
an ability to convey technical material through formal written reports which satisfy accepted standards for writing style
PO-8
the ability to analyze and use existing literature
PO-9
the ability to demonstrate effective oral communication skills
PO-10
the ability to stay abreast of advancements in the area of computer engineering