Applied Evolutionary Algorithms

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

• 60 pts final exam (written part)
• 18 pts labs
• 22 pts projects

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

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.

Algorithms inspired by nature represent strong techniques for solving many difficult optimization tasks. Also, it has been shown for several times that evolutionary algorithms may provide innovative solutions to some problems which are out of the scope of existing methods. Therefore it is important to provide a course about evolutionary algorithms and their applications as a part of expertise to expectant engineers in the area of information technology.

• 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. Differential evolution.
7. Ant colony optimization.
8. Particle swarm optimization.
9. Statistical evaluation of experiments.
10. Models of computational development.
11. Fundamentals of multiobjective optimization.
12. Advanced algorithms for multiobjective optimization.
13. Applications of evolutionary algorithms.

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.

None.

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