BIMSI16-Swarm Intelligence and Artificial Life

Module Provider: School of Biological Sciences
Number of credits: 10 [5 ECTS credits]
Terms in which taught: Autumn term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2018/9

Module Convenor: Prof Slawomir Nasuto


Type of module:

Summary module description:

There has been a lot of interest in creaating models of nautral systems. Some of these work can help us inderstand the principles of operation of the natural systems; other approaches focus on practical applications of information processing capacity found in natural systems. The course will look at two domains, swarm intelligecne and artifical life. It will provide an overview of swarm intelligence algorithms, as biologically inspired methods of solving challenging problems and will position them within a systematic framework. Artifical life will overview the field which tries to use various artifical models of living systems that help us understand the basic building blocks of life as it is, and as it could be.


Swarm Intelligence and Artificial Life are two active areas of research in computational optimisation and modelling. This module aims to inspire students into exploring the creative potential of these fields as well as providing insight into the state-of-the-art knowledge of the SI techniques, their fundamental properties and relationships. Understanding of the SI in the context of search and optimisation including no free lunch theorem and its constructive use.

Assessable learning outcomes:
The debate between higher purpose (teleology) and emergence will be examined. Theoretical understanding of life and life-like phenomena will be tested. Current scientific issues concerning attempts at synthesising life will be evaluated.

The mechanisms of operation of the individual Swarm Intelligence optimisation and search techniques will be discussed. Their place in a common framework and the similarities and differences will be examined.

Additional outcomes:
The philosophical, social and ethical issues surrounding the creation of soft, hard & wet artificial life are important considerations for this subject.

Outline content:

Swarm Intelligence

Overview of field; stochastic search and optimisation; classification of SI techniques; A selection of techniques from: Particle Swarm; Stochastic Diffusion Processes; Evolutionary Algorithms; Ant Search; Memetic and Cultural algorithms; Ant Colony Optimisation; Estimating Density Algorithms; Bayesian Optimisation Evolution;

Artificial Life

Overview of artificial life – covering Hard, Soft and Wet A Life – and history

Soft Alife includes : Boids, Cellular Automata, Game of Life, Evolutionary Computing and ALife, Daisyworld, Modelling Populations, Attractors, Discrete models, Self similarity and Fractals, and ALife for TV and Films.

Hard Alife includes: robots, swarming, self replicating and flocking; Methods for Learning; Evolutionary Robotics; Machine Consciousness

Wet ALife includes Belousov-Zhabotinsky reaction, Animats, In Vitro Neuronal Nets; Evolutionary Biology; Epigenetics; Baldwin Effect; Unconventional computing ;

Wetware computing; synchronisation and Small World theory

Brief description of teaching and learning methods:

Lectures will be illustrated with the examples of systems and concepts discussed

Contact hours:
  Autumn Spring Summer
Lectures 20
Guided independent study 80
Total hours by term 100.00
Total hours for module 100.00

Summative Assessment Methods:
Method Percentage
Oral assessment and presentation 30
Set exercise 70

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Each student presents to the class a seminar in a symposium setting based on a research journal paper on a significant advance in the field. This carries 30% of the final mark. Each student writes a WWW blog of another topic, illustrating the basic concepts, explaining research in the area and creating an app or simulation which allow to deepen understanding of the underlying principles.  (70%)

Formative assessment methods:

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information:

Assessment requirements for a pass:
MEng 50% overall module mark
MSc 50% overall module mark

Reassessment arrangements:

Reassessment arrangements in August/September: resubmission of coursework.

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 20 April 2018


Things to do now