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Research home page for Simon Harding |
This web page is organized into the following sections:
· Contact Information
· Research interests and publications
o Evolution in materio, Analogue Computing and Unconventional Computation
o Genetic programming and Developmental Systems
o Implementation of Genetic Programming
· Brief CV
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Address |
Department Of Computer Science, Memorial University of Newfoundland, St John’s NL, Canada A1B 3X5 |
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Telephone |
+1 709 737 4891 |
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Skype |
simon.l.harding |
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Web |
Evolution In
Materio www.evolutioninmaterio.com General Purpose Genetic Programming on GPUs www.gpgpgpu.com Department of Computer Science,
Memorial University www.cs.mun.ca |
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Evolution
in materio, Analogue Computing and Unconventional
Computation |
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In
1958 Gordon Pask described a method of manipulating a physical system (a
series of electrodes in a chemical chamber) so that it would carry out a new function.
This work was largely forgotten for many years. In 1996, Adrian Thompson
demonstrated that when artificial evolution is sufficiently unconstrained it
is possible for it to exploit physical properties for computation. He
demonstrated this using an reprogrammable electronic device called an Field
Programmable Gate Array (FPGA). This
inspired the idea that artificial evolution might be able to used to discover
hitherto unknown ways of configuring matter to carry out computation. This
led to a research project that demonstrated that artificial evolution
can be used to manipulate a liquid crystal device so that it can do
computations that solve a number of tasks (frequency discrimination, robot
control, Boolean logic). I
am particularly interested in the application of evolution to in materio computation. It is my position that the best way to use
material for computation is to allow evolutionary algorithms to treat the
system as a black box. We can
therefore shield ourselves from our
inability to understand what is happening inside the material system and
still obtain computation. These
ideas were the focus of my doctoral thesis: Simon
Harding PhD Thesis, University of York, 2006 |
Evolution In Materio Simon
Harding and Julian F. Miller 2007,
entry accepted for publication in the Encyclopedia
of Complexity and System Science |
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Programming the Extended Analogue Computer using
Evolutionary Algorithms Simon Harding 2007, Under
preperation |
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Evolution
in Materio: Exploiting the Physics of Materials for Computation Simon
L. Harding, Julian F. Miller and Edward A. Rietman 2006,
Available at arxiv.org submitted to the International Journal of Unconventional Computing |
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A Framework for the Automatic Identification and
Extraction of Computation from Materials Simon Harding,
James Neil, Klaus-Peter Zauner, Julian F. Miller, and Kester Clegg 2006,
Unpublished technical report |
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Evolution in Materio: Exploiting the Physics of
Materials for Computation Simon
L. Harding, Julian F. Miller and Edward A. Rietman Position
paper at the ‘The Grand Challenge in Non-Classical Computation International
Workshop’, York, 2005 |
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Evolution In Materio: Investigating the Stability of
Robot Controllers Evolved in Liquid Crystal Simon Harding and
Julian F. Miller Published in
ICES 2005 International Conference on Evolvable Systems |
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Evolution In Materio:Evolution In Materio : Evolving
Logic Gates in Liquid Crystal Simon
Harding and Julian F. Miller Presented
at the Workshop on Unconventional Computing at ECAL 2005 VIIIth European
Conference on Artificial Life, winner of best paper award in Workshop in
Unconventional Computing. To be published in International Journal of
Unconventional Computing |
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Evolution In Materio:A Real-Time Robot Controller in
Liquid Crystal Simon Harding
and Julian F. Miller Published in the
proceedings of 2005 NASA/DoD Conference on Evolvable Hardware |
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Evolution in materio: Initial experiments with liquid
crystal Simon
Harding and Julian Miller Proceedings
of 2004 NASA/DoD Conference on Evolvable Hardware (EH'04) , June 2004,
Seattle, Washington, USA |
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Evolution in materio: A Tone Discriminator In Liquid
Crystal Simon Harding
and Julian Miller In Proceedings
of the Congress on Evolutionary Computation 2004 (CEC'2004) |
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A scalable platform for intrinsic hardware and in
materio evolution S.
L. Harding and Julian Francis Miller NASA/DOD
Conference on Evolvable Hardware, 2003 |
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My
research interest in genetic programming and developmental systems is focused
on practical applications. The Self
modifying CGP I developed aims to develop a general purpose genetic
programming system that allows for growth and development. The
representation is a graph that encodes both the computational elements and
the instructions needed for growth/modification. The growth/ modification is environmentally
sensitive – data coming into the graph determines which instructions are
operated. The
system automatically can decide if development is needed – simply by evolving
out the modification instructions . Work
so far shows that the system can evolve large modular systems (patterns,
Boolean logic circuits) as well as behave as a normal GP system where modification is not needed (for
regression, classification etc). Adding
the mechanism for modification to this system introduced no overhead in terms
of evolability. Therefore, this
representation should be appropriate to use when it is unclear if
development(or any form of self modification) is needed. Future
work will investigate further uses of the representation for tasks such as
growth of artificial neural networks. I
am also interested in the exploitation, during development, of the physical
properties of the system and how these can be used to benefit artificial
developmental systems. |
Self-modifying Cartesian Genetic Programming Simon
L. Harding , Julian F. Miller and Wolfgang Banzhaf GECCO
2007 |
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The Dead State: A comparison between developmental and
direct encodings. Simon Harding
and Julian F Miller GECCO 2006 |
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A comparison between developmental and direct encodings:
An update of the GECCO 2006 Paper “The Dead State” Simon
Harding and Julian F Miller 2006,
Unpublished technical report |
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Evolution of Robot Controller Using Cartesian Genetic
Programming. Simon Harding
and Julian F. Miller EuroGP 2005 |
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Pole Balancing With Cartesian Genetic Programming Simon
Harding, Rachel M. Harris and Julian F. Miller 2005,
Unpublished technical report. |
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Fitness
evaluation in genetic programming is typically a bottle neck. Parallelization
is one mechanism to overcome this problem. A
traditional approach is to use a cluster of computers. However, modern graphics cards – Graphics
Processing Units (GPUs) – can also be used. GPUs
are cheap, highly parallel (200+ processors on recent cards) and fast (1.3GHz
per processor). Their SIMD
architecture makes them ideally suited for GP use – where we typically have
one evolved program and many test cases to evaluate. I
have shown that on a standard laptop, a speed increase of 30times can be
expected when using the GPU to run fitness evaluations. |
Evolution of Image Filters on
Graphics Processor Units Using Cartesian Genetic Programming Simon Harding To
appear in WCCI 2008. |
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Genetic Programming on GPUs for Image Processing Simon Harding
and Wolfgang Banzhaf Submitted to GECCO
2008 |
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Fast Genetic Programming on GPUs Simon
Harding and Wolfgang Banzhaf EuroGP
2007 |
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Fast Genetic Programming and Artificial Developmental
Systems on GPUs Simon Harding
and Wolfgang Banzhaf 21st Annual
International Symposium on High Performance Computing Systems and
Applications (HPCS 2007) |
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A Distributed Evolutionary Algorithm Using C Sharp and
Mono Simon
Harding 2006,
Unpublished technical report |
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Employment |
Post
doctoral research fellow, Jan 2008 Department
Of Computer Science, Memorial University, Canada Collaborateur
scientifique, May 2007 – Jan 2008 Laboratory
of Intelligent Systems, EPFL, Lausanne, Switzerland Primarily
researching the use of an bio-inspired analog network encoding and its
applicability to evolving robust electronic circuits. Post
doctoral research fellow, Feb 2006 –
May 2007 Department
Of Computer Science, Memorial University, Canada Primarily
researching the properties of genetic
programming developmental systems. Other research involves physics based
computation, using cluster-based simulations of complex systems and evolving
control strategies. Other research investigated high performance
implementation of these systems. Consultant
Control Software Engineer, Sept
2001-Present Software
In Control Ltd. Consultancy
in real time control and remote telemetry. Current projects include high
precision, non-contact laser measurement systems and robotic control (for
Scantron Industrial Products), remote wireless web-cams (for BBC) and telemetry
over GSM networks (PIC Ltd). Role
includes development on multiple platforms (Windows XP/Embedded, CE and
Linux) and PLC units. Teaching
Assistant, Sept 2001- Dec 2004 School
Of Computer Science, University Of Birmingham Teaching
undergraduates and masters students, in both laboratory demonstrations and
small group tutorials. Courses taught include: Java, neural networks,
robotics and AI programming. Software
Engineer, Jun 2001- Sept 2001 Delcam,
Birmingham Full
time position, developing CAD file format conversion applications and
upgrading existing application for OLE automation. Control
Engineer, Jun 1997 –Sept 2000 Honeywell
Control Systems, Bracknell Apprentice
control engineer until Sept 1998. Undergraduate degree was sponsored by Honeywell. Worked full time as control engineer during
holiday periods 1998-2000. Roles included: site visits for equipment
configuration, repair and calibration and distributed control system
development for large-scale projects for the petrochemical industry. |
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Education |
The University
of York, 2004-2005 PhD Electronics Thesis title:
“Evolution In Materio” Partially funded
by the “European Office of Aerospace Research and Development” Examined by Prof
A. Tyrrell and Prof. W. Banzhaf The University of
Birmingham, 2001-2004 PhD |