Applied Evolutionary Algorithms

• 26 hrs lectures
• 12 hrs pc labs
• 14 hrs projects

• 60 pts final exam
• 18 pts labs
• 22 pts projects

Survey about actual optimization techniques and evolutionary algorithms for solution of complex, NP complete problems. To learn how to solve typical complex tasks from engineering practice using evolutionary techniques.

Ability of problem formulation for the solution on the base of evolutionary computation. Knowledge of analysis and design methods for evolutionary algorithms.

• Brabazon, A., O'Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-43630-1
• Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd ed. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-44873-1
• Kvasnička, V., Pospíchal, J., Tiňo, P.: Evolučné algoritmy. STU Bratislava, Bratislava, 2000, ISBN 80-227-1377-5
• Talbi, E.-G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken, New Jersey, 2009, ISBN 978-0-470-27858-1
• Luke, S.: Essentials of Metaheuristics. Lulu, 2015, ISBN 978-1-300-54962-8

• Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford, 1996, ISBN 978-0195099713

• Brabazon, A., O'Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-43630-1

• Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd ed. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-44873-1

• Jansen, T.: Analyzing Evolutionary Algorithms. Springer-Verlag, Berlin Heidelberg, 2013, ISBN 978-3-642-17338-7

1. Principles of stochastic search algorithms.
2. Monte Carlo methods.
3. Evolutionary programming and evolution strategies.
4. Genetic algorithms.
5. Genetic programming.
6. Statistical evaluation of experiments.
7. Ant colony optimization.
8. Particle swarm optimization.
9. Differential evolution.
10. Applications of evolutionary algorithms.
11. Models of computational development.
12. Fundamentals of multiobjective optimization.
13. Advanced algorithms for multiobjective optimization.

1. Basic concepts of evolutionary computing, typical problems, solution of a technical task using a variant of Metropolis algorithm.
2. Evolutionary algorithms in engineering areas, optimization of electronic circuits using genetic algorithm.
3. Evolutionary design using genetic programming.
4. Edge detection based on ant algorithms.
5. Differential evolution-based optimization of neural networks.
6. Solution of a selected task from statistical physics.

Realisation of individual topics from the area of evolutionary computation.

Evaluated practices, project. In the case of a reported barrier preventing the student to perform scheduled activity, the guarantor can allow the student to perform this activity on an alternative date.

Computer practices, project submission, final exam.

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