Computing Science Seminars, Spring 2013
The Division of Computing Science and Mathematics presents the following seminars. Unless otherwise stated, seminars will take place in Room 4108 of the Cottrell Building, University of Stirling from 15.00 to 16.00 on Friday afternoons during semester time. For instructions on how to get to the University, please look at the following routes.
Spring 2013
| Date | Presenter | Title/Abstract |
|---|---|---|
| Friday 15 Feb |
Dr.
David R.White School of Computing Science, University of Glasgow |
A Raspberry Pi Cloud: The Glasgow
Raspberry Pi Cloud is a scale model of a cloud datacentre,
built from Raspberry Pi devices and Lego. In this talk, I
will explain what motivated us to construct such a thing,
how the Pi Cloud was built, and how we intend to use it
for both teaching and research |
| Friday 22 Feb |
No Seminar |
|
| Friday 1 March |
Dr. John
Woodward Computing Science and Mathematics, University of Stirling |
In this Paper we Propose 10,000
Algorithms: The (Semi-)automatic Design of (Heuristic)
Algorithms: Heuristics aim to provide
practical solutions to computationally intractable
problems. A researcher traditionally proposes a novel
algorithm and demonstrates its comparatively better
performance on a set of benchmark problem instances. It
will be argued that this is a theoretically flawed
approach, and instead of manually designing single
algorithms we can automatically generate vast numbers of
algorithms for the problem class at hand. Three examples of automated algorithm development will be given; these domains include bin packing, components of a genetic algorithm and probability distributions. In all cases, the automatically-designed algorithm statistically outperforms the human designed counterpart. |
| Friday 8 March |
Dr.
Julian F. Miller Department of Electronics, University of York |
Abstract Cartesian Genetic Programming:
Cartesian Genetic Programming (CGP) is a form
of automatic program induction that uses an
evolutionary algorithm to evolve graph-based
representations of computational structures. It is a
highly flexible and general technique that can find
solutions in many problem domains (e.g. neural networks,
mathematical equation induction, object recognition in
images, function optimization, digital and analogue
circuit design, combinatorial optimization, etc.). Since
its invention in 1999, it has been developed and
made more efficient in various ways. It can automatically
capture and evolve sub-functions (known as modules) and
through the introduction of self-modification operators it
is possible to find mathematically provable general
solutions to classes of problems. This talk is given by
the inventor of the technique. The first edited book on
CGP was published by Springer in September 2011. For
further information CGP has its own dedicated website:
http://www.cartesiangp.co.uk |
| Friday 15 March |
Dr. William (Bill) Langdon CREST, Department of Computer Science, University College London | Genetic Improvement Programming:
Genetic programming, can optimise software and software
engineering, including evolving test benchmarks, search
meta-heuristics, protocols, composing web services,
improving hashing and garbage collection, redundant
programming and even automatically fixing bugs. There may
be many ways to balance functionality with resource
consumption. But a human programmer cannot try them all.
Also the tradeoff may be different on each differnet
hardware platform and could vary over time or as usage
changes. It may be that GP can automatically create
different trade offs for each new market. Recent results
include substantial speed up by generating a new version
of a large tool for a special case. Note: Bill Langdon has written 3 books in Genetic Programming, including the recent: A Field Guide to Genetic Programming, which can be downloaded for free. He also maintains a comprehensive and up to date Genetic Programming Bibliography. |
| Friday 22 March |
Kevin
Swingler Computing Science and Mathematics, University of Stirling |
Complexity and Order:
Complexity is caused by interactions. Systems have low
complexity when their parts are independent or interact
only in small groups. We call them low order systems. On
the other hand, high order systems have many interacting
parts and, consequently, high complexity. This talk
presents a neural network approach to modelling
mixed-order complex systems of binary variables in a way
that makes the interactions explicit |
| Friday 29 March |
No Seminar, Semester Holidays (Good Friday) | |
| Friday 5 April |
No Seminar, Semester Holidays | |
| Friday 12 April |
Dr. Simon
Poulding Department of Computer Science, University of York |
Exploiting Graphics Cards for
High-Performance General Purpose Computation:
Graphics cards render high-resolution, high-framerate
graphics in real time in order to support the demands of
applications such as gaming. To achieve this performance,
the architecture of the graphics processing unit (GPU) is
designed to efficiently execute a large number of threads
in parallel, and to minimise the overhead of memory
access. Intriguingly, many modern graphics cards
facilitate the use of this high-performance parallel
computing environment for purposes other than graphics
rendering: a technique known as general purpose computing
on graphics processing units (GPGPU). In this talk, I will discuss the architecture and development of GPGPU applications, and describe two recent examples from my research: a fast estimation of distribution algorithm and high-volume software reliability testing. |
| Friday 19 April |
Dr.
Mauricio de Souza Department of Production Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil. |
Two industrial application of scheduling
models: In this talk we present two industrial
applications for which scheduling models can lead to
significant savings in cost. The first application is the
production control for the short term in a metal industry
where a flexible machine assisted by CAM software is used
to manufacture precision parts. The second application is
the scheduling of jobs for continuous casting in a steel
industry. Jobs correspond to ladles containing melt steel
with certain chemical properties and required slab widths
as well. We present mixed integer models developed for
each application, and we report numerical results on real
instances showing that with the use of scheduling models
significant gains with respect to practice can be
achieved. |
| Friday 26 April |
No Seminar |
|
| Wednesday 1 May, Room 2A73, Time 12:30 |
Prof.
Frederic Saubion Faculty of Sciences, University of Angers, Angers, France |
Autonomous Search for Combinatorial
Optimization: Decades of innovations in
combinatorial problem solving have produced better and
more complex algorithms. These new methods are better
since they can solve larger problems and address new
application domains. They are also more complex, which
means that they are hard to reproduce and often harder to
fine tune to the peculiarities of a given problem. This
last point has created a paradox where efficient tools
became out of reach for practitioners. Autonomous search represents a new research field defined to precisely address the above challenge. Its major strength and originality consist in the fact that problem solvers can now perform self-improvement operations based on analysis of the performances of the solving process -- including short-term reactive reconfiguration and long-term improvement through self-analysis of the performance, offline tuning and online control, and adaptive control and supervised control. Autonomous search "crosses the chasm" and provides engineers and practitioners with systems that are able to autonomously self-tune their performance while effectively solving problems. In this talk, we review existing work and we attempt to classify the different paradigms that have been proposed during past years to build more autonomous solvers. We also draw some perspectives and future directions. Note: This is an invited talk as part of a Workshop to be held at Stirling. Funded by SICSA (The Scotish Informatics & Computer Science Alliance), Complex Systems Engineering theme, SEABIS (Self-Organising, Emergent, Autonomous, Biologically Inspired Systems ). Workshop Organisers: Gabriela Ochoa and David Cairns. More details can be found here. |
| Previous Seminar Series |
|---|
| 2012: Spring Autumn |
| 2011: Spring Autumn |
| 2010: Spring Autumn |
| 2009: Spring Autumn |
| 2008: Spring Autumn |
| 2007: Spring Autumn |
| 2006: Spring Autumn |
| 2005: Spring Autumn |
| 2004: Spring Autumn |
| 2003: Spring Autumn |
Last Updated: 04 April 2013. Top image (constructed highly
multimodal landscape) courtesy of Dr. Michael G. Epitropakis.