Course overview
History of evolutionary computation; major areas: genetic algorithms, evolution strategies, evolution programming, genetic programming, classifier systems; constraint handling; multi-objective cases; dynamic environments; parallel implementations; coevolutionary systems; parameter control; hybrid approaches; commercial applications.
Course learning outcomes
- Explain evolutionary computation techniques and methodologies set in the context of modern heuristic methods.
- Apply various evolutionary computation methods and algorithms for particular classes of problems.
- Develop evolutionary algorithms for real-world applications.
- Use scientific research papers and present them in a seminar talk.