Computational Intelligence


Understand the basic structures and the learning mechanisms underlying neural networks within the field of artificial intelligence and examine how synaptic adaptation can facilitate learning and how input to output mapping can be performed by neural networks.

Obtain an overview of linear, nonlinear, separable and non separable classification as well as supervised and unsupervised mapping.

Understand the benefit of adopting naturally inspired techniques to implement optimisation of complex systems and acquire the fundamental knowledge in various evolutionary techniques.

Become familiar with the basic concepts of systems optimisation and its role in natural and biological systems and entities.


For Neural Networks (part I), 12 lectures delivering the following chapters:

  • Introduction: Chapter 1
  • Structural Aspects: Chapter 2
  • Learning Processes: Chapter 3
  • Single-Layer Perceptrons: Chapter 4
  • Multi-Layer Perceptrons: Chapter 5
  • Radial-basis Function Networks: Chapter 6
  • Support Vector Machines: Chapter 7
  • Self-Organising Maps: Chapter 8
For Evolutionary Computation (part II), 12 lectures delivering the following chapters:
  • Introduction: Chapter 1
  • Genetic Algorithms: Chapter 2 (basic elements), Chapter 3 (advanced topics), Chapter 4 (theoretical analysis)
  • Genetic Programming: Chapter 5 (genetic programming & gene expression programming)
  • Evolutionary Strategies: Chapter 7 (overview and extensions)

Recommended Texts

Part I:

  • S. Haykin, Neural networks; a comprehensive foundation, New Jersey, Prentice Hall.

Part II:

  • Genetic Algorithms: in search optimisation and machine learning, DE Goldberg.   
  • Genetic Algorithms + Data Structures = Evolution Programs, Z Michalewic.

Part I:

  • C. Bishop, Neural networks for pattern recognition, Oxford University Press.
  • C. Bishop, Pattern recognition and machine learning, Springer-Verlag.
  • R. Duda, P. Hart, D. Stork, Pattern classification, Wiley and Sons.
  • S. Theodoridis, K. Koutroumbas, Pattern recognition, Academic press.

Part II:

  • Genetic Programming: An Introduction, W Banzhaf, P Nordin, RE Keller, FD Francone.
  • An Introduction to Genetic Algorithms for Scientists and Engineers , DA Coley.
  • A Genetic Algorithm Tutorial, Darrell Whitley.​

Learning Outcomes

​Learning  the advantages and main characteristics of neural networks in relation to traditional methodologies. Also, familiarity with different neural networks structures and their learning mechanisms.
​​​​Appreciation of the advantages of evolutionary-related approaches for optimisation problems and their advantages compared to traditional methodologies. Also, understanding the different techniques of evolutionary optimisation for discrete and continuous configurations.
Understanding of the needs for genetic encoding and modelling for solving optimisation problems and familiarisation with the evolutionary operators and their performance.​​​​​​Understanding of the neural network learning processes and their most popular types, as well as  appreciation of how neural networks can be applied to artificial intelligence problems.

Learning Strategy

Slide based presentations and blackboard for both lectures and tutorials

This module will be delivered through a combination of formal lectures and tutorials. Fully comprehensive notes have been designed to cover the major and most commonly used types of neural networks (part I) and also the most popular types of evolutionary optimisation (part II). Extra material will be offered to the students optionally to enhance their learning experience.