ITI41222 Evolutionary Computation (Autumn 2024)

This course gives an insight into different evolutionary computation methods and their applications.

The topics covered in this course are:

• A general overview of the field of evolutionary computation
• The underlying principles and theory of evolutionary computation
• A detailed overview of the leading evolutionary, bio-inspired, social and other nature-inspired algorithms
• Multi-objective evolutionary algorithms
• Issues with evolutionary computation techniques: parameter control, convergence, diversity, elitism, etc.
• Applications of evolutionary computation methods in solving science, industry and real-world search/optimization/classification tasks
• Advanced and recent trend in evolutionary computation

Knowledge

The student

• understand the basic principles of evolutionary computation.
• gets an overview of different types of evolutionary computation methods
• becomes familiar with the benefits and drawbacks of these methods
• can implement these methods to solve problems in practical, industrial and other complex domains

Skills

The student can

• formulate a real-world problem as an evolutionary search/optimization/classification problem by deciding on necessary representations and evolutionary operators
• decide the suitable evolutionary algorithms/techniques based on the nature of the applications
• implement different evolutionary algorithms/techniques for such problems by using a package and/or coding his/her own algorithms

General competence

The student gains insight into evolutionary, biologically and nature inspired algorithms/techniques for search, optimization, and classification; and becomes capable of working with such algorithms and techniques.

The student must deliver:

• Up to 3 mandatory exercises
• Attend one individual oral presentation after submitting the announced exercises. During the oral presentation, the student must explain the implementation details of the submissions and demonstrate a certain level of efficiency for the submitted solutions to the exercise.

Coursework requirements must be accepted to qualify for the exam.

Individual portfolio and written exam

The exam is divided into two parts:

• The individual portfolio consists of up to three exercises and determines 50% of the final grade.
• The individual written exam is based on the course curriculum and determines 50% of the final grade. Duration 3 hours. No supporting materials allowed.

Both parts of the exam must be passed to pass the exam as a whole. The student will get an individual joint grade for the entire course. Grades: A - F.

This course is evaluated by a:

• Final course evaluation (compulsory)

The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee at the Department of Computer Science and Communication.