Fuzzy systems and evolutionary computation

Learning Results

The course aims at: (a) providing a solid theoretical grounding and practical skills, along with in-depth knowledge regarding main notions of Computational Intelligence, (b) establishing the importance of these scientific fields in computer science, as well as the wide range of their applications in computing systems. The courses objectives include introducing concepts, models, algorithms, and tools in order to develop intelligent systems. Example topics include Fuzzy and Neurofuzzy Systems, Genetic Algorithms and Swarm Intelligence. Moreover, emphasis is put on real-world applications, along with hands-on experience and practice on dedicated software (MATLAB, OCTAVE).

Upon successful completion of the course students:

• will acquire knowledge of the principles, procedures and applications of the scientific fields of Fuzzy Systems and Evolutionary Computation.
• will delved into the methods and algorithms of Fuzzy Systems and evolutionary Computation and acquire the appropriate skills to implement these algorithms, as well as the practical experience, having become familiar with specialized software packages.
• will study applications of Computational Intelligence to real problems, in order to acquire specialized problem-solving skills, which are required in research and/or innovation in order to develop new knowledge and processes, especially in multidisciplinary fields,
• will acquire the necessary learning skills that will allow them to continue their studies in the field of Computational Intelligence in an autonomous fashion, to a large extent.

Skills acquired

• Search, analysis and synthesis of data and information, using the necessary technologies
• Individual work
• Group work
• Work in an multi-disciplinary environment
• Production of new research ideas
• Creative and critical thinking

• Fuzzy set theory, fuzzy rules and fuzzy rule bases, compositional rule of inference
• Structure and operation of Mamdani fuzzy systems
• Takagi–Sugeno fuzzy systems
• Neurofuzzy systems
• Genetic algorithms
• Swarm intelligence
• Fuzzy systems for time-series modelling
• Application of evolutionary computation to fraud detection in data networks
• Fuzzy and neurofuzzy systems for noise cancellation in audio signals
• Evolutionary computation systems for satellite image processing
• Neurofuzzy systems for biomedical signal processing

1. R. Babuska, Computational Intelligence in Modelling and Control, Delft University of Technology, 2009.
2. E. Cox, Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration, Morgan Kaufmann Publishers, 2005.
3. A.P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley & Sons, 2006.
4. A.P. Engelbrecht, Computational Intelligence: An Introduction, 2nd Edition, Wiley, 2007.
5. D. Goldberg, Genetic Algorithms in Search optimization and Machine Learning, Addison-Wesley Pub. Co., 1989.
6. K.A. de Jong, Evolutionary Computation, MIT Press, 2002.
7. J. Keller, D. Liu, D. Fogel, Fundamentals of Computational Intelligence – Neural Networks, Fuzzy Systems, and Evolutionary Computation, IEEE Press – Wiley, 2016.
8. S. Rajasekaran, G. Vijayalakshmi, Neural Network, Fuzzy Logic, and Genetic Algorithms: Synthesis and Applications, PHI Editions, 2013.
9. T. Ross, Fuzzy Logic with Engineering Applications, John Wiley & Sons, 2016.
10. B. Schuller, Intelligent Audio Analysis, Springer, 2013.
11. S. Sivanandam, S. Sumathi, S. Deepa, Introduction to Fuzzy Logic Using MATLAB, Springer, 2007.
12. L. Tsoukalas, R. Uhrig, Fuzzy and Neural Approaches in Engineering, John Wiley & Sons, 1997.

Related scientific journals:

1. Transactions on Fuzzy Systems, IEEE.
2. Transactions on Evolutionary Computation, IEEE.
3. Fuzzy Sets and Systems, Elsevier.
4. Swarm end Evolutionary Computation, Elsevier.