F21BC Biologically Inspired Computation

Dr Michael LonesDr Wei PangDr Mohammad Hamdan

Course co-ordinator(s): Dr Michael Lones (Edinburgh), Dr Wei Pang (Edinburgh), Dr Mohammad Hamdan (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

Course Description: Link to Official Course Descriptor.

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

Location: Edinburgh.

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 a range of biologically inspired computation methods, their limitations and areas of applicability.

Ability to apply one or more biologically inspired techniques in solving a practical problem.

Learning Outcomes: Personal Abilities

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

  • Identify and define approaches that can be used to apply bio-inspired methods to existing problems in optimisation and machine learning.
  • Exercise substantial autonomy and initiative (courseworks) (PDP)
  • Demonstrate critical reflection (courseworks) (PDP).

Assessment Methods:

Assessment: Examination: (weighting – 50%) Coursework: (weighting – 50%)
Re-assessment:  Examination: (weighting – 100%)

SCQF Level: 11.

Credits: 15.