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 optimization and evolutionary algorithms: types of evolutionary algorithms, "No Free Lunch" (NFL) theorem.
• Hill climbing algorithm and genetic algorithm: basic components of a genetic algorithm (selection, crossover, mutation).
• Classical evolutionary algorithms: differential evolution, particle swarm optimization, ant colony optimization.
• Parameter control in evolutionary algorithms: manual tuning, adaptive control, self-adaptive control, meta-evolutionary approaches.
• Exploration and exploitation in evolutionary algorithms.
• Genetic programming.
• Handling constraints in optimization.
• Multimodal and multi-criteria optimization.
• Dynamic optimization.
• Combinatorial optimization.
• Comparison of different evolutionary algorithms.
• Examples of practical applications 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 %.
