Department of Informatics

6CCS3BIM Biologically Inspired Methods
KEATS MODULE PAGE
Credit value: 15
Semester: 2

Aims
Computational methods inspired by evolution, by nature, and by the brain are being used increasingly to
solve complex problems in engineering, informatics, robotics and artificial intelligence. They are
particularly useful in areas such as optimisation, pattern recognition, scheduling and intelligent control
where traditional mathematical and computation methods fail. In such domains, biological ideas have
provided valuable models for successful problem solving strategies. The aim of this module is to introduce
students to a range of important biologically-inspired algorithms and techniques and to establish a
practical understanding of the real-world problems to which these techniques may be fruitfully be applied.

Learning Outcomes
• BIM 1: Awareness of biologically-inspired engineering techniques and of the multidisciplinary
background to such methods.
• BIM 2: Appreciation of how new algorithmic approaches to optimisation can emerge from lateral
thinking and ideas from other disciplines.
• BIM 3: Understanding of a range of nature-inspired methods and the ability to apply them to solve
real-world problems.
• BIM 4: Ability to design genetic algorithms and ant-colony optimisation to solve combinatorial
optimisation problems.
• BIM 5: Ability to recognise future opportunities for exploiting biological inspiration in solving
combinatorial optimisation problems.

Syllabus
• Concept of optimisation
• Traditional combinatorial optimisation methods and complexity classes
• Nature-Inspired Methods: General local search methods, Genetic algorithms, Simulated annealing,
Advanced topics of nature-inspired heuristic strategies, Neural Networks.
• Behaviour-Inspired Methods: Ant colony optimisation, particle swarm optimisation
• Applications of biologically inspired methods

Assessment
100% written examination (2 hours).