Objectives and competences
The objective of this course is for students to be able to demonstrate understanding of theoretical basis of evolutionary algorithms, to analyze components of evolutionary algorithms, and to design new variants of evolutionary algorithms.
Content (Syllabus outline)
• Introduction to evolutionary algorithms: classification of evolutionary algorithms, No Free-Lunch Theorem.
• Genetic algorithms: schema theorem, genetic operators (selection, crossover, mutation).
• Genetic programming: schema theorem of genetic programming.
• Parameter control in evolutionary algorithms: parameter tuning, adaptive parameter control, self-adaptive parameter control, meta-evolutionary algorithms.
• Multi-modal and multi-criteria optimization.
• Exploration and exploitation in evolutionary algorithms.
• Artificial Bee Colony (ABC) and Teaching-Learning-Based Optimization (TLBO).
• Other evolutionary algorithms: differential evolution, particle swarm optimizations, ant colonies.
• Comparison of evolutionary algorithms.
• Examples in usage of evolutionary algorithms.
Learning and teaching methods
• lectures,
• lab work.
Intended learning outcomes - knowledge and understanding
On completion of this course the student will be able to
• explain the theoretical basis of evolutionary algorithms,
• compare different evolutionary algorithms,
• select the best evolutionary algorithms for requested problem,
• design new variants of evolutionary algorithms.
Intended learning outcomes - transferable/key skills and other attributes
Communication skills: oral lab work defence, manner of expression at written examination.
Use of information technology: use of frameworks for evolutionary algorithms.
Problem solving: problem solving with evolutionary algorithms.
Readings
• A. E. Eiben, J. E. Smith: Introduction to Evolutionary Computing, Springer-Verlag, Berlin, 2003.
• D. Simon: Evolutionary Optimization Algorithms, John Wiley & Sons, 2013.
Additional information on implementation and assessment The exam may be replaced by written midterm examinations in the weight of 50 %.
