School of Computer Science
MSc/PGDip/PGCert in Natural Computation
Natural computation is the study of computational systems that use ideas
and get inspirations from natural systems, including biological,
ecological and physical systems. It is an emerging interdisciplinary area
in which techniques and methods are studied for dealing with large,
complex, and dynamic problems. This exciting programme will cover a number
of topics, such as evolutionary algorithms, co-evolution, evolutionary design,
nature-inspired optimisation techniques, evolutionary games, novel
learning algorithms, artificial neural networks, theory of natural
computation, molecular computation and quantum computation. The primary
aim of this advanced MSc is to provide a solid foundation in natural
computation for graduates to pursue a research and/or development career
in industry or to pursue further studies (e.g., PhD).
A very good honours degree or equivalent in Computer Science/Engineering
or a closely related area.
Industrial Advisory Board
This MSc programme is supported by the EPSRC through its Master's Level
Training Packages and by a number of leading companies. An Industrial Advisory
Board has been set up to provide advices and feedback to the MSc programme.
The current Board members include:
This is an advanced 12 month programme consisting of 180 credits.
The whole programme is divided into three terms of 60 credits each. Students
who do not wish to pursue the whole MSc programme may enroll in
Postgraduate Certificate (PGCert, 60 credits) or Postgraduate Diploma (PGDip,
120 credits). Part-time students are welcome.
programme has a strong emphasis on research and research skills in comparison
with some other taught MSc programmes. The following paragraphs give an
overview of this advanced MSc programme. More up-to-date module descriptions and
syllabuses can be found at
There are three taught modules (10 credits each) and one mini-project
(30 credits). All are compulsory.
- Introduction to Molecular and Quantum Computation (10 credits):
This module covers the basic ideas, techniques and algorithms in molecular and
quantum computation. Examples of such ideas, techniques and algorithms include
DNA algorithms, molecular gates, parallel computation
models executable by DNA-based chemical processes, DNA inference engine,
quantum Turing machine, quantum cryptography,
quantum search, evolving quantum algorithms, and computation with executable
- Introduction to Neural Computation (10 credits): This module
provides the first introduction to the most common topics in neural
computation, including biological neural networks, artificial neuron
models, feed-forward artificial neural networks, learning and generalisation,
back-propagation, radial basis function networks, self-organising maps,
and committee machines.
- Introduction to Evolutionary Computation (10 credits):
Evolutionary computation is the study of computational systems that use ideas
and get inspirations from natural evolution. Its techniques can be applied to
optimisation, learning and design. Example topics covered in this module
include natural and artificial evolution, genetic algorithms, chromosome
representations, search operators, genetic programming, automatically
defined functions, schema theorems, and classifier systems.
- Mini-project (30 credits): This consists of two parts. The first
is a taught module on Research Skills and
the second is a research training project that aims at providing various
transferable skills that are necessary for conducting successful research,
includes literature search, critical literature review,
oral and written communication skills, scientific paper and report writing,
problem solving, etc.
There are three taught advanced modules (10 credits each) and one
mini-project (30 credits). All are compulsory. The three advanced modules are
organised around problems (i.e., optimisation, learning and design), not
- Nature Inspired Optimisation (10 credits): This module introduces
a range of nature-inspired techniques and algorithms for both real-valued and
combinatorial optimisation. Examples of such techniques and algorithms include
global optimisation by evolutionary algorithms, self-adaptation, advanced
operators, ant colony optimisation, swarm optimisation, simulated
annealing, hybrid algorithms, constrained handling techniques, multi-objective
optimisation, optimisation in a dynamic environment.
- Nature Inspired Learning (10 credits): This module presents
main learning techniques inspired by natural, physical and biological systems.
Examples of such techniques include learning classifier systems, competitive
and cooperative coevolution, evolutionary games, learning through
genetic programming, support vector machines, adaptive resonance theory neural
networks, Boltzman neural networks, interaction between learning and
evolution, Red Queen effect, Baldwin effect, Lamarkian evolution,
- Nature Inspired Design (10 credits): This module introduces the
basic ideas of nature-inspired design techniques. Different
algorithms and their applications will be presented.
Similarities and differences
between these techniques/algorithms and other classical
techniques will be discussed
whenever appropriate. The design domain ranges from
architectural, engineering and
graphics design to electronic circuit design and
evolvable hardware. Examples of such
techniques include evolution and knowledge discovery,
circuit design by evolution, novel
architectural design by evolution, creative design,
interactive evolution, evolutionary
graphics, knowledge extraction from evolution,
extrinsic evolvable hardware, intrinsic
evolvable hardware, on-line adaptation, and
- Mini-project (30 credits): This module consists of a research
project on the in-depth investigation of a chosen topic
coming from industry (strongly encouraged) or academe. The topic must be
different from that chosen for the first mini-project in Term I
because it is crucial for a student to gain in-depth knowledge in
different areas and to be able
to apply natural computation techniques to different problems. This
mini-project is expected to be
extended further into the summer project in the following term.
Term III (Summer Term)
There is a compulsory project (60 credits) in the summer, solving a
substantial real world problem using natural computation techniques (including
hybrid techniques). Industrial co-supervisors will be used whenever
appropriate. This project requires students to apply the knowledge and skills
they acquired previously in the course to solve a difficult real-world problem.
In 2001/02, these were GBP2805 per year for UK/European Union students;
GBP9600 per year for overseas students.
can be found from
- Several fully funded EPSRC studentships, covering fees and maintenence
costs, are available each year. They will be awarded on a competitive basis
according to applicants' academic achievements. We are sorry that
international students are not eligible for these studentships.
- In addition to the above funding which is specific to this course,
students (both UK and international) are also eligible to apply for
other studentships and scholarships.
Contact for Further Information
The Admissions Secretary
School of Computer Science
University of Birmingham
Within the UK:
phone: 0121-414 4782
fax: 0121-414 4281
Outside the UK:
phone: +44 121-414 4782
fax: +44 121-414 4281
Admissions Tutor and Course Director:
Prof. Xin Yao.
Computer Science Home Page (http://www.cs.bham.ac.uk/)
Page maintained by: Prof. Xin Yao.