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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.

If you would like to give a seminar to the department in future or if you need more information,  
please contact the seminar organiser,  .

Spring 2013

Date Presenter Title/Abstract
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
22 Feb
No Seminar

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.
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:
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.
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
29 March
No Seminar, Semester Holidays (Good Friday)
5 April
No Seminar, Semester Holidays
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.
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.
26 April
No Seminar
1 May, Room
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.

Last Updated: 04 April 2013. Top image (constructed highly multimodal landscape) courtesy of Dr. Michael G. Epitropakis.