Evolutionary algorithms Course

Course on Evolutionary Algorithms

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Basic information on the course:

course annotation on Study Programmes pages (look at Intelligent Systems - MS level)
detail information

Class time and location - Summer term
- Lectures: Tuesdays, 10:50-12:20 V010
- Practicals: Wednesdays, 10:50-12:20 V010
Staff
- Lectures: Marian Mach (faculty)
- Practicals: Marian Mach (faculty)
Lecture main topics (for details see presentations in Mind maps section)
- Inspiring principles and basic algorithm structure
- Basic building blocks of EA
- Special usage of EA (niche, multicriterial, non-stationary, constrained)
- Practical algorithm setting
Practicals topics
- Experiments with algorithm blocks (selection, genetic operators)
- Using EA to solve different problem types (~5 problem types)
- Applications of EA (1 problem, 1 presentation)
Requirements for successful course completion
- Compulsory practical attendance (max 3 absences)
- Compulsory lecture attendance (max 3 absences)
- To be awarded with overall credit > 50% (credit for practicals > 20%, final exam credit > 30%)
Grading
- Practicals: 40%
- Final exam: 60%

	Evolutionary algorithms: elements and principles
	Evolutionary algorithms: problem solving

2024
- Stable matching: problem definition (data for SM, data for SMI, data for SMT, data for SMTI), validator (linux executable)
- Car rent facility: problem definition
- Pickup and delivery: problem definition (data for PD),

	Simple version of EA (search for function extreme)
	Bias/spread of selection methods: experiment notebook, data (Spring Term 2017)
	Takeover-time: experiment notebook,
	Selection drift: experiment notebook,
	Material reduction by selection methods: experiment notebook, data (Spring Term 2019)
	Search potential of crossover operators: data