F20BC Biologically Inspired Computation

Dr Michael LonesDr Wei PangAssoc Prof. Hadj Batatia

Course co-ordinator(s): Dr Michael Lones (Edinburgh), Dr Wei Pang (Edinburgh), Assoc Prof. Hadj Batatia (Dubai), Mohamed Serry (Dubai).


Traditional computation finds it either difficult or impossible to perform a certain key range of tasks associated with pattern recognition, problem solving and autonomous intelligence. Great progress towards designing software for such tasks has emerged by taking inspiration from a range of natural, mainly biological, systems.

The aims of this course are to:

  • introduce an appreciation of the former
  • introduce the main biologically-inspired algorithms and techniques which are now commonly researched and applied
  • establish a practical understanding of the real-world problems to which these techniques may be fruitfully be applied.

Detailed Information

Pre-requisite course(s): F29AI Artificial Intelligence and Intelligent Agents or equivalent.

Location: Dubai, Edinburgh, Malaysia.

Semester: 1.


  • Classical vs. Biologically-inspired computation,
  • evolutionary algorithms (basic EA design, and how they are applied to a wide range of problems)
  • swarm intelligence (ant colony methods, particle swarm optimisation)
  • neural computation (perceptrons, multilayer perceptrons, associative networks)
  • cellular automata

Learning Outcomes: Subject Mastery

Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Informed Learning)

  • Understanding of limitations of traditional computation.
  • A critical understanding of the two most common biologically inspired computation methods, their limitations and areas of applicability.
  • Understanding of how to apply one or more biologically inspired techniques in solving a practical problem.

Learning Outcomes: Personal Abilities

ndustrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT

  • Identify approaches that can be used to apply bio-inspired methods to existing problems in optimisation and machine learning.
  • Exercise reasonable levels of initiative in working with a bio-inspired method (courseworks) (PDP)
  • Demonstrate a degree of critical reflection in assessing the performance of a bio-inspired method (courseworks) (PDP).

SCQF Level: 10.

Credits: 15.