COMP8715 Heuristic Optimisation GE

This topic provides a thorough exploration of the heuristic optimization techniques of computational intelligence, with a working language of C/C++ for examples and assignments.

Specific areas covered include:

1. Theory and practice of neural networks (NN) including feedforward networks, backpropagation, learning machines, self-organizing networks, and deep learning
2. Theory and practice of evolutionary algorithms (EA) including genetic algorithms, genetic programming, pareto optimization and selection methods
3. Theory and practice of fuzzy methods (FZ) including fuzzy logic, type 1 and 2 models, and soft clustering
4. Theory and practice of Bayesian methods (BY) including Bayesian statistics and belief networks (BN)
5. Practical experience writing, deploying and evaluating heuristic optimization algorithms
6. Practical applications of heuristic, reasoning and optimization techniques in areas such as speech and language processing, character and image recognition, data base and data mining
7. Practical understanding of the application nature-inspired methods and the process of modelling and simulating natural processes both for general computing use and in the natural domain

This topic aims to provide a theoretical and practical introduction to the development and application of heuristic and nature-inspired techniques common in Computational Intelligence (CI). Students will understand the mathematical and algorithmic principles behind CI and gain hands-on experience in employing CI techniques towards solving practical problems in a variety of domains. The scope of the topic incorporates increasingly relevant applications such as biometrics, speech and computer vision and the ethical issues associated with these. Students will also obtain skills in analytical thinking, experimental research, and effective communication within the wider context of CI.

1. Understand literature and applications associated with Computational Intelligence (CI) and communicate effectively in this field
2. Appreciate the common principles and best practices linking the constituent domains of CI
3. Utilize and integrate CI techniques towards solving a variety of relevant real-world problems
4. Evaluate and compare in a scientific manner the suitability and effectiveness of different CI techniques
5. Deal with the practical and ethical constraints associated with CI-based systems