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Computational Intelligence

COURSE: Computational Intelligence

Code: ФЕИТ01007

ECTS points: 6 ECTS

Number of classes per week: 3+0+0+3

Lecturer: prof. Tatjana Kolemishevska-Gugulovska, PhD

Subject of the course content:

Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which traditional approaches, i.e., first principles modelling or explicit statistical modelling, are ineffective or infeasible. Many such real-life problems are not considered to be well-posed problems mathematically, but nature provides many counterexamples of biological systems exhibiting the required function, practically. Traditional models also often fail to handle uncertainty, noise and the presence of an ever-changing context. Computational Intelligence provides solutions for such and other complicated problems and inverse problems. Computational intelligence systems usually comprise hybrids of paradigms such as artificial neural networks, fuzzy systems, and evolutionary algorithms, augmented with knowledge elements, and are often designed to mimic one or more aspects of biological intelligence.

Topics that will be covered in this course are as follows: 1. Background:  Brief review of biological and behavioral motivations for the constituent methodologies of computational intelligence. 2. Computational Intelligence: concepts of adaptation and self-organization; Relationships among the three major components of CI (evolutionary computation, neural networks, and fuzzy systems) and how they cooperate and/or are integrated into a CI system. 3. Evolutionary Computation: The main paradigms of evolutionary computation: genetic algorithms, evolutionary programming, evolution strategies, genetic programming, and particle swarm optimization; 4. Evolutionary Computation Implementations: Issues to be considered when implementing evolutionary computation paradigms. 5. Artificial Neural Networks: Neural network components and terminology; Review of neural network topologies; Neural network learning and recall; Hybrid networks and recurrent networks; The issues of pre-processing and post-processing. 6. Neural Network Implementations: Issues to be considered when implementing artificial neural networks. 7. Fuzzy Systems: Design and analysis of fuzzy systems; Issues and special topics related to fuzzy systems. 8. Fuzzy System Implementations: Issues to be considered when implementing fuzzy systems. 9. Computational Intelligence Implementations.

Literature:

  1. Eberhart, E. and Y. Shi., “Computational Intelligence: Concepts and Implementations”, Morgan Kaufmann, San Diego, 2007.
  2. Andries P. Engelbrecht, “Computational Intelligence: An Introduction, 2nd Edition”, John Wiley, 2007
  3. Konar, A., Gary Riley, “Computational Intelligence: Principles, Techniques, and Applications”, Springer, Berlin, Germany, 2005.
  4. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, “Neuro-Fuzzy and Soft Computing (A Computational Approach to Learning and Machine Intelligence)”, Prentice Hall; 1 edition, 1997.
  5. Fogel, D.B. and C.J. Robinson (Eds), “Computational Intelligence: The Experts Speak”, John Wiley, New York, 2003.