Evolutionary and Adaptive Systems (2014 entry)

MSc, 1 year full time/2 years part time

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Computing at Sussex was ranked in the top 15 of UK universities for research in the 2008 Research Assessment Exercise (RAE). 95 per cent of our research was rated as internationally recognised or higher, with 70 per cent rated as internationally excellent or higher, and one-fifth rated as world leading. 

Computing at Sussex has excellent teaching and facilities and was ranked 21st in the UK in The Guardian University Guide 2014, 22nd in the UK in The Times Good University Guide 2013 and 30th in the UK in The Complete University Guide 2014. 

We have many successful business collaborations, enabling our taught courses to be informed by industry and facilitating exciting research. 

Our graduates are highly employable, with over 85 per cent entering employment in IT and related industries.  

Our portfolio of postgraduate degrees is designed to meet the needs of students who want to develop a career in the IT industry and those wishing to move into academia or a research career. 

We provide an intellectually stimulating environment with research in areas including pervasive computing technology, digital media and graphics, e-business, human-computer interaction, adaptive systems and artificial life, cognitive systems, natural language processing, and artistic and creative systems. 

Programme outline

The study of natural and artificial evolutionary and adaptive systems is at the heart of important emerging approaches to artificial intelligence, cognitive science, computational biology, complex systems and related areas. 

Sussex is internationally renowned for its research in these interdisciplinary areas and has one of the largest groups in this field. 

This course provides a solid grounding in the major themes of the area, including artificial life, adaptive systems, biologically inspired robotics, complex adaptive systems, dynamical systems approaches to cognition, evolutionary systems and evolutionary computing, and natural and artificial neural systems, as well as the opportunity to take options in cutting-edge areas such as the science of consciousness. 

This well-established course (the first to be set up in this field) is taught by leading experts and there are many opportunities to interact with the thriving local community of researchers in this area. Students have access to specialist facilities including robotics labs. 

Assessment 

You are assessed by coursework, unseen examinations, essays, programming projects and a 12,000-word dissertation. 

We continue to develop and update our modules for 2014 entry to ensure you have the best student experience.In addition to the course structure below, you may find it helpful to refer to the 2012 modules tab.

Full-time course structure

Autumn term: Artificial Life • Intelligence in Animals and Machines • Mathematics and Computational Methods for Complex Systems • Object-Oriented Programming (students with a high level of programming experience may take Advanced Software Engineering). 

Spring term: Adaptive Systems • Neural Networks. You also take two options from Computational Neuroscience • Generative Creativity • Image Processing • Machine Learning • Neuroscience of Consciousness • Sensory and Motor Functions of the Nervous System. 

Summer term: you undertake a dissertation project under the supervision of a member of faculty, which is usually based on a programming project. This will give you the opportunity to develop further what you have learnt in the context of a piece of research. It is not unusual for work from dissertation projects to be published in conference proceedings or journals. 

Part-time course structure

The part-time structure for each degree is as follows: 

Year 1: in each of the autumn and spring terms you take two modules. In the summer term you undertake work on the dissertation. 

Year 2: you take two modules in the autumn term. In the spring and summer terms you complete work on the dissertation. 

Back to module list

Adaptive Systems

15 credits
Spring teaching, year 1

During this module you will gain familiarity with a number of different approaches to modelling and understanding adaptive processes in natural and artificial systems. This module covers recent work in AI that is geared towards understanding intelligence, both in natural and artificial systems, in terms of the generation of adaptive behaviour in autonomous agents acting in dynamic uncertain environments. Adaptation is studied at both the evolutionary and the lifetime scale.

Topics include: cybernetic roots of AI; frameworks for adaptive behaviour; evolutionary theory; genetic algorithms; somatic adaptation; classical control theory; fuzzy control; autonomous robots (behaviour based, subsumption architecture, evolutionary robotics); reinforcement learning; Q-learning; learning classifier systems; reinforcement learning in neural networks; applied reinforcement learning; and methods of analysis and modelling.

Advanced Software Engineering

15 credits
Autumn teaching, year 1

This course will revise project management skills, waterfall and agile models for software development, and UML. You will also be introduced to modern software development practices for use in the group project; software architectures, including component based, service based and aspect oriented software; development approaches for concurrency; and Domain Specific Languages

Artificial Life

15 credits
Autumn teaching, year 1

This module provides you with an introduction to the new field of artificial life. The module has a dual focus: first in bringing computing ideas from biology to AI that are useful in synthesising hardware and software-lifeline artefacts, and secondly using computational tools for testing ideas in biology.

Topics that you will study include: cellular automata and random Boolean networks; models of growth and development; introduction to evolutionary algorithms; dynamical system approaches to cognition; coevolution; fitness landscapes; and information theory and life.

Computational Neuroscience

15 credits
Spring teaching, year 1

Computational neuroscience is an approach to understanding the functioning of the nervous system by modelling it at many different structural levels with computational techniques. This module gives you an overview of the field.

Topics that you will study include: nerve cells; the nervous system and the study of behaviour; real and computational neurons; single neuron models; numerical methods for neuronal modelling; modelling motor systems in simple organisms; reconstructing small neural networks; and simulating large-scale neural networks.

Generative Creativity

15 credits
Spring teaching, year 1

The module will introduce the use of generative creativity (GC) in a variety of areas selected from music, art, architecture, design, engineering and literature. The majority of the module will focus on examining a number of case studies that demonstrate the variety of approaches taken by existing GC systems. The module teaching is very much hands-on and programming is required. Although the module is not primarily philosophical in orientation, it will involve discussion on the nature of creativity, its definition and evaluation, by referring to the work of creativity researchers in a variety of disciplines from cognitive science to psychology.

Image Processing

15 credits
Spring teaching, year 1

 You will study: introduction to machine vision and relation to image processing; importance of scene constraints and methods of achieving these in the industrial environment; lighting techniques; ;radiometric and photometric units; camera technologies; lenses for machine vision; image formation and resolution; display technologies; image acquisition hardware and hardware implemented point operations; histogram manipulations; linear invariant systems theory in two dimensions; the convolution operation and its discrete implementation as mask operators; first and second differential edge detection operators; edge filling techniques; Hough transform; the 2-D Fourier transform and frequency domain filters; 2-D correlation. Scene segmentation methods and region filling; pattern recognition techniques: shape descriptors; Fourier descriptors; template matching; and examples of machine vision systems in industry.

Intelligence in Animals and Machines

15 credits
Autumn teaching, year 1

This module will develop your understanding of what it means for an animal or a machine to behave intelligently, and how brain and behavioural systems are adapted to enable an animal to cope effectively within its environment. You will explore this topic in lectures and seminars through a number of case studies that are designed to acquaint you with recent behavioural and AI literature.

Topics include: robots and biology; insect navigation; path planning; memory and flexible behaviour; motion detection and flow fields in insects; artificial neural networks; object recognition and categorisation; social organisation of foraging; social learning; and why primates' brains are big.

Machine Learning

15 credits
Spring teaching, year 1

This module looks at data mining and machine learning. You will consider the main classes of problem and review the methods that are appropriate in each case.

Mathematics and Computational Methods for Complex Systems

15 credits
Autumn teaching, year 1

This module provides you with a foundation in mathematical and scientific computing techniques used widely in artificial intelligence, artificial life and related fields. The material covered in this module will give you the knowledge necessary to study a number of options on other MSc modules, and is a prerequisite for the Neural Networks and Computational Neuroscience modules. Coursework is based around Matlab packages.

Topics include: vectors and matrices; differential calculus; numerical integration; probability and hypothesis testing; dynamical systems theory.

Neural Networks

15 credits
Spring teaching, year 1

On this module you will study a variety of contemporary approaches to neural networks. You will be introduced to the theory underlying these approaches, and learn how to implement them as computer programs in MATLAB. The approaches covered include biological and statistical foundations of neural networks, perceptron, BP, SVM, RBFN and competitive learning. You will also be given a brief introduction to information theory and its applications in neural networks.

Topics that you will study include: biological background; learning; perceptron; back propagation; RBF networks; support vector machines, competitive learning; and PCA, ICA, information theory and neural networks.

Neuroscience of Consciousness

15 credits
Spring teaching, year 1

Consciousness is one of the last remaining frontiers of scientific exploration, and theories and methods in neuroscience are at the front line of this endeavour. Topics covered on this module are likely to include: measuring and studying consciousness; states of consciousness (including wake, dreaming, hypnosis and vegetative state); visual consciousness (including the different roles of visual cortex and fronto-parietal network; blindsight and neglect as disorders of visual awareness); implicit learning and meta-knowledge; psychiatric disturbances of consciousness (eg hallucinations, depersonalisation); interoceptive awareness; consciousness and cortical plasticity (examples of synaesthesia, phantom limb and sensory substitution); computational models of consciousness; biological models of consciousness; and evolutionary approaches to consciousness.

Object Oriented Programming

15 credits
Autumn teaching, year 1

You will be introduced to object-oriented programming, and in particular to understanding, writing, modifying, debugging and assessing the design quality of simple Java applications.

You do not need any previous programming experience to take this module, as it is suitable for absolute beginners.

Sensory and Motor Functions of the Nervous System

15 credits
Spring teaching, year 1

Perceiving and acting upon the environment is something at which humans are expert. How does this ability to represent and act on visual and other sensory entities come about? Cognitive neuroscience is a diverse and interdisciplinary field of study that investigates the complex interplay of mental and brain function. This module provides an in-depth survey and analysis of behavioural observations, theoretical accounts, electrophysiological studies and imaging results on selected topics in cognitive neuroscience. Topics include: connections between sensory and motor function; cross-modal interaction; movement and event perception; development and plasticity of the nervous system; neural prediction and visual constancies; and compensation.

Back to module list

Entry requirements

UK entrance requirements

A first- or upper second-class undergraduate honours degree. Undergraduate studies generally need to have been in disciplines requiring both numeracy and computer literacy. Students from other backgrounds who can demonstrate numeracy and computer literacy may also be considered. Mature applicants with relevant experience will be considered on an individual basis.

Overseas entrance requirements

Overseas qualifications

If your country is not listed below, please contact the University at E pg.enquiries@sussex.ac.uk

CountryOverseas qualification
Australia Bachelor (Honours) degree with second-class upper division
Brazil Bacharel, Licenciado or professional title with a final mark of at least 8
Canada Bachelor degree with CGPA 3.3/4.0 (grade B+)
China Bachelor degree from a leading university with overall mark of 75%-85% depending on your university
Cyprus Bachelor degree or Ptychion with a final mark of at least 7.5
France Licence with mention bien or Maîtrise with final mark of at least 13
Germany Bachelor degree or Magister Artium with a final mark of 2.4 or better
Ghana Bachelor degree from a public university with second-class upper division
Greece Ptychion from an AEI with a final mark of at least 7.5
Hong Kong Bachelor (Honours) degree with second-class upper division
India Bachelor degree from a leading institution with overall mark of at least 60% or equivalent
Iran Bachelor degree (Licence or Karshenasi) with a final mark of at least 15
Italy Diploma di Laurea with an overall mark of at least 105
Japan Bachelor degree from a leading university with a minumum average of B+ or equivalent
Malaysia Bachelor degree with class 2 division 1
Mexico Licenciado with a final mark of at least 8
Nigeria Bachelor degree with second-class upper division or CGPA of at least 3.0/4.0
Pakistan Four-year bachelor degree, normally with a GPA of at least 3.3
Russia Magistr or Specialist Diploma with a minimum average mark of at least 4
South Africa Bachelor (Honours) degree or Bachelor degree in Technology with an overall mark of at least 70%
Saudi Arabia Bachelor degree with an overall mark of at least 70% or CGPA 3.5/5.0 or equivalent
South Korea Bachelor degree from a leading university with CGPA of at least 3.5/4.0 or equivalent
Spain Licenciado with a final mark of at least 2/4
Taiwan Bachelor degree with overall mark of 70%-85% depending on your university
Thailand Bachelor degree with CGPA of at least 3.0/4.0 or equivalent
Turkey Lisans Diplomasi with CGPA of at least 3.0/4.0 depending on your university
United Arab Emirates Bachelor degree with CGPA of at least 3.5/4.0 or equivalent
USA Bachelor degree with CGPA 3.3-3.5/4.0 depending on your university
Vietnam Masters degree with CGPA 3.5/4.0 or equivalent

If you have any questions about your qualifications after consulting our overseas qualifications, contact the University at E pg.enquiries@sussex.ac.uk

English language requirements

IELTS 6.5, with not less than 6.5 in Writing and 6.0 in the other sections. Internet TOEFL with 88 overall, with at least 20 in Listening, 20 in Reading, 22 in Speaking and 24 in Writing.

For more information, refer to English language requirements.

Visas and immigration

Find out more about Visas and immigration.

For more information about the admissions process at Sussex

For pre-application enquiries:

Student Recruitment Services
T +44 (0)1273 876787
E pg.enquiries@sussex.ac.uk

For post-application enquiries:

Postgraduate Admissions,
University of Sussex,
Sussex House, Falmer,
Brighton BN1 9RH, UK
T +44 (0)1273 877773
F +44 (0)1273 678545
E pg.applicants@sussex.ac.uk 

Fees and funding

Fees

Home UK/EU students: £5,5001
Channel Island and Isle of Man students: £5,5002
Overseas students: £16,2003

1 The fee shown is for the academic year 2013.
2 The fee shown is for the academic year 2013.
3 The fee shown is for the academic year 2013.

To find out about your fee status, living expenses and other costs, visit further financial information.

Funding

The funding sources listed below are for the subject area you are viewing and may not apply to all degrees listed within it. Please check the description of the individual funding source to make sure it is relevant to your chosen degree.

To find out more about funding and part-time work, visit further financial information.

Chancellor's International Scholarship (2014)

Region: International (Non UK/EU)
Level: PG (taught)
Application deadline: 1 May 2014

25 scholarships of a 50% tuition fee waiver

Fulbright-Sussex University Award (2014)

Region: International (Non UK/EU)
Level: PG (taught)
Application deadline: 15 October 2013

Each year, one award is offered to a US citizen for the first year of a postgraduate degree in any field at the University of Sussex.

Leverhulme Trade Charities Trust for Postgraduate Study (2014)

Region: UK
Level: PG (taught), PG (research)
Application deadline: 1 October 2013

The Leverhulme Trade Charities Trust are offering bursaries to Postgraduate students following any postgraduate degree courses in any subject.

Santander Scholarship (2014)

Region: International (Non UK/EU)
Level: PG (taught)
Application deadline: 1 May 2014

Two scholarships of £5000 fee waiver for students studying any postgraduate taught course.

USA Friends Scholarships (2014)

Region: International (Non UK/EU)
Level: PG (taught)
Application deadline: 3 April 2014

Two scholarships of an amount equivalent to $10,000 are available to nationals or residents of the USA on a one year taught Master's degree course.

Faculty interests

Research groups 

Research is a core activity of the Department of Informatics and is organised around our interdisciplinary research groups. Our research often entails collaborations between the groups, as well as with other academic schools at Sussex and external academic, institutional and commercial partners. The research groups are briefly described below, for more details, visit the Department of Informatics.

Cognitive and Language Processing Systems

The research of this group addresses the science and engineering of complex systems for cognitively demanding and language-intensive domains, including the application of methods from cognitive science and natural-language engineering. The group focuses on searching and classifying free text (eg medical records) in large quantities, cognitive processes of writing and drawing, cognitive modelling of processes such as attention and graphical production, and cognitively informed interactive tools for complex problem solving, decision making, instruction and learning. 

Faculty research interests include: 

Professor John Carroll Natural language parsing, acquiring knowledge about words from text, sentiment analysis, clinical text mining. 

Professor Peter Cheng The nature of representational systems (diagrams for complex problem solving, discovery and conceptual learning), processes of writing and drawing. 

Dr Bill Keller The use of language technology to support communication and interaction, language-aware technology, applications of distributional models of natural language semantics. 

Dr David Weir Controlling non-determinism in natural language generation, language in pervasive computing environments, efficient parsing, inferring knowledge about words from raw text. 

Dr Sharon Wood Multi-agent systems. Cognitive modelling, in particular information acquisition through cognitively plausible visual attention processes, and knowledge-based reasoning.  

Evolutionary and Adaptive Systems (EASy) 

The EASy group has been internationally prominent since it was established in the early 1990s. It is concerned with the interfaces between the biological and computational sciences, particularly with reference to furthering understanding of brains and minds. The group’s research is highly interdisciplinary and involves many strong links with other departments at Sussex. Research foci include adaptive and cognitive robotics, artificial life, bio-inspired computational methods, computational neuroscience, creative systems, history and philosophy of AI and ALife, clinical applications of neural modelling, machine learning, scientific studies of consciousness, and synthetic neuroethology. It runs the highly successful Centre for Computational Neuroscience and Robotics (CCNR) jointly with Sussex Neuroscience in the School of Life Sciences. Members of the group also direct the Centre for Research in Cognitive Science (COGS) and the Sackler Centre for Consciousness Science (SCCS), both important cross-campus initiatives. 

Faculty research interests include: 

Dr Luc Berthouze Motor development in infants and in machines, EEG-based brain-machine interfaces, epigenetic robotics, and modelling cognitive development with robotic systems. 

Professor Margaret Boden Computational approaches in the philosophy of mind and psychology, purpose and creativity, philosophy of AI and ALife, and social implications of AI. 

Dr Ron Chrisley Non-conceptual representation; philosophy of cognitive science, AI, mind, consciousness, computation; computer/robotic models of visual experience, emotion, creativity. 

Professor Phil Husbands Evolutionary and adaptive robotics, evolutionary computation, ALife, computational neuroscience, adaptive systems, neuromodulation, history of AI, creative systems. 

Dr Thomas Nowotny Information processing in nervous systems, sequence learning in neuronal systems, accurate conductance-based neuron models, and hybrid systems. 

Dr Andy Philippides Computational neuroscience and neuroethology, evolutionary robotics, insect visual homing strategies, and gaseous neuromodulators in neural networks. 

Professor Anil Seth Theoretical neuroscience and evolutionary and adaptive systems; time-series analysis of neural dynamics, neurorobotics; and evolutionary theory and ecological modelling. 

Dr Chris Thornton Computational learning using symbolic algorithms and connectionist mechanisms, and theories of creativity.  

Foundations of Software Systems

This group is interested in the theory and practice of future computation and communication. We build mathematical theories of computation and use such models to inform the design of programming languages and compilers. We design and evaluate distributed applications and services that provide the foundations of the pervasive computing infrastructure and other software systems. We model and analyse data representing system configurations, social networks, trust, and provenance. 

Faculty research interests include: 

Dr Martin Berger Concurrency theory, semantics and pragmatics of programming languages, program logics, metaprogramming, computer science methods in theoretical biology. 

Dr Dan Chalmers The design of pervasive computing systems, particularly those which process and consider emotion, social networks, trust and context to enable efficient and usable system behaviour. 

Dr Ian Mackie Foundations of programming languages and models of computation. Applying techniques from mathematical logic and quantum mechanics to programming language implementation. 

Dr Bernhard Reus Mathematical semantics of programming languages and their foundations; constructive logic; and techniques and tools supporting program analysis, design and verification. 

Dr Ian Wakeman Networks and distributed systems, emphasis on design from the user perspective, pervasive computing, computational trust. 

Interactive Systems

This research group operates at the intersection between people and digital technology. We are interested in interaction in the broadest sense and consider it in relation to both traditional desktop-based technologies and more novel digital technologies, including mobile, immersive, ubiquitous and pervasive technologies. 

We are interested in users of all kinds, ranging from experts using technology in their work (such as medical professionals) and experts developing technology for their work (such as programmers) to novices of all kinds (from children using educational technology through to adults using social technology). 

Our research focuses on human-computer interaction, accessibility, music informatics, learning with and through technology (including social networks), technology-enhanced social interaction, new models of e-business, e-government and e-citizenship, tangible and embodied interaction, motion capture techniques, building virtual worlds for digital heritage and other applications, real-time animation, digital content creation and digital video. 

Faculty research interests include: 

Dr Natalia Beloff New models of e-business, business models for small and medium digital enterprises, adver-gaming, advertising for social networks, education and social networks. 

Dr Judith Good Constructivist learning environments, game-based learning, technology toolkits for learning, visual programming languages, learner-centred and participatory design methodologies. 

Dr Paul Newbury Multimedia systems, in particular virtual prototyping, ubiquitous systems and digital content creation. Technology-enhanced learning and video techniques for distance learning. 

Dr Phil Watten Software development; virtual prototyping; high-level design; system modelling; display systems; interface design; and media production, new media and web broadcasting. 

Dr Martin White 3D graphics; virtual, augmented and mixed reality; animation; motion sensing; motion gaming; digital heritage systems; interaction; work flows. 

Interdisciplinary research centres 

The Department of Informatics plays a central role in the following major interdisciplinary research centres: 

Centre for Computational Neuroscience and Robotics (CCNR) 

CCNR is a collaboration between the Department of Informatics and the School of Life Sciences. This thriving centre seeks to explore and exploit the interfaces between the biological and computational sciences. CCNR is jointly run with the Evolutionary and Adaptive Systems group. 

Centre for Research in Cognitive Science (COGS) 

COGS is an internationally recognised centre for interdisciplinary investigation into the nature of cognition, be it natural or artificial. Staff in Informatics and Psychology, as well as Sussex linguists, focus on teaching and research. 

Sackler Centre for Consciousness Science (SCCS) 

SCCS is a venture between the Department of Informatics, the School of Psychology, and the Brighton and Sussex Medical School. The Centre’s aim is to unravel the complex neural mechanisms underlying conscious experience by bringing together computational modelling, cognitive neuroscience, and clinical applications. 

Careers and profiles

Our graduates have gone on to work in the software, entertainment and financial sectors for major companies such as BT, HP, Logica, Accenture, Sega, IBM, Sony, the BBC, NaturalMotion and Capgemini, as well as successfully setting up their own companies. Many have gone on to PhD study and now work in academic or industrial research. 

Dan's career perspective

Dan Cowan

‘The MSc in Evolutionary and Adaptive systems was great. Embracing an unusual mixture of biology, mathematics, computer science and philosophy, we were encouraged to apply inspiration from the natural word to technological questions, and conversely to utilise technological innovation in understanding natural systems. In the working world this ability to think outside the box and across disciplines is a valuable asset.

‘A couple of colleagues and I founded a company to commercialise the AI techniques studied during the programme. Three years after Codefarm Software was founded, we were solving complex problems in the investment banking world. Last year we sold the company to Calypso Technology, a major player in the trading systems market. Currently I head Calypso Technology’s Brighton-based Galapagos development unit, looking after a team of highly skilled engineers and technicians (many with MSc qualifications from Sussex). We specialise in applied evolutionary search algorithms.

‘The MSc I studied at Sussex has been an integral part of my career journey, giving me a unique and marketable body of skills along with good personal contacts.’

Dan Cowan
Director of Engineering, Galapagos,
Calypso Technology

For more information, visit Careers and alumni.

School and contacts

School of Engineering and Informatics

The School of Engineering and Informatics brings together the areas of mechanical and electrical engineering with informatics, in particular computer science and artificial intelligence, and product design.

Department of Informatics

The Department of Informatics is an internationally renowned centre for teaching and research in computer science, and provides the skills and knowledge required for a future in this dynamic field. 

Informatics, Postgraduate Admissions,
University of Sussex, Falmer,
Brighton BN1 9QJ, UK
T +44 (0)1273 678195
F +44 (0)1273 877873
E informaticsoffice@sussex.ac.uk
Department of Informatics

Postgraduate Open Day 2013

4 December 2013, 1pm-4pm
Bramber House, University of Sussex

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