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
- Understand evolutionary approaches to solving complex optimisation problems.
- Identify and develop application-specific problem representations and fitness metrics.
- Design and implement genetic algorithms to solve non-continuous valued problems.
- Design and implement evolutionarystrategies to solve continuous valued problems.
- Analyse results and solutions to verify their correctness and identify sources of error.
- Critique state-of-the-art scientific publications in evolutionary computing.