Traditional computation finds it either difficult or impossible to perform a wide range of tasks including product design, decision making, logistics and scheduling, pattern recognition and problem solving. However, nature is proven to be highly adept at solving problems making it possible to take inspiration from these methods and to create computing techniques based on natural systems. This module will provide you with the knowledge to create and apply techniques based on evolution, the intelligence of swarms of insects and flocks of animals, and the way the human brain is thought to process information. This module is appropriate for you if you have an interest in optimisation and data analysis, and have some programming and mathematical experience.
Non-requisite module - ECM3412
This module aims to provide you with the necessary expertise to create, experiment with and analyse modern nature-inspired algorithms and techniques as applied to problems in industry and industrially-motivated research fields such as operations research.
The module also aims to provide you with knowledge of the limitations and advantages of each algorithm and the expertise to determine which algorithm to select for a given problem,
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
On successful completion of this module, you should be able to:
Module Specific Skills and Knowledge:
1 demonstrate deep understanding of the difficulties associated with certain intelligence-related tasks that we would wish to program computers to do;
2 comprehend and implement several diverse nature-inspired algorithms, and appreciate the circumstances and environments in which they are best employed.
Discipline Specific Skills and Knowledge:
3 implement software for addressing either large-scale real-world scheduling and optimisation problems or complex real-world pattern recognition problems; 4 comprehend software for producing lifelike simulations of certain natural behaviours.
Personal and Key Transferable/ Employment Skills and Knowledge:
5 analyse and choose appropriate techniques for given problems from a very diverse toolbox of methods;
6 understand how new ideas in science and engineering can emerge from lateral thinking and ideas from other disciplines;
7 synthesise and succinctly communicate information from publications in the field to individuals unfamiliar with the material;
SYLLABUS PLAN - summary of the structure and academic content of the module
- classical vs. nature-inspired computation;
- evolutionary algorithms (including genetic programming and multi-objective evolutionary algorithms);
- ant colony optimisation;
- particle swarm optimisation;
- swarm intelligence;
- neural computation (including multi-layer perceptrons and self-organising maps);
- artificial life;
- cellular automata;
- immune system methods.
