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From:
John Woodward <[log in to unmask]>
Reply To:
John Woodward <[log in to unmask]>
Date:
Wed, 25 Mar 2015 07:01:55 +0000
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Please forward to math and computer science students who might be interested

1. Fully Funded Places Available on Stirling's MSc. in Big Data
http://www.cs.stir.ac.uk/courses/msc-bd/funded.html

2. Fully Funded Phd positions at Stirling on Search Based Software
Engineering http://www.cs.stir.ac.uk/~jrw/positions/positions.html

3. 5th Workshop on Evolutionary Computation for the Automated Design of
Algorithms http://web.mst.edu/~tauritzd/ECADA/

(details below, and also via links)

===================================================================================================================

1. Fully Funded Places Available on Stirling's MSc. in Big Data
http://www.cs.stir.ac.uk/courses/msc-bd/funded.html

Fully Funded Places Available on Stirling's MSc. in Big Data

The government has identified Big Data skills as essential for our economy
to prosper. Big Data expertise is already in high demand and attracting
higher salaries due to the skills shortage.

To help meet the demand for Big Data skills, the Scottish Funding Council
is paying the fees for 40 students to study Big Data and Data Science in
selected Scottish Universities, including Stirling. If you are a resident
of Scotland or the EU, you can apply to have all of your fees paid and
learn Big Data skills and technologies in Stirling's beautiful campus in
the heart of Scotland.

Interested? Here are some links that will tell you all you need to know:

Online prospectus for MSc. in Big Data
Course web pages, including lecture notes, schedules, and lab sheets
Our Big Data jobs feed page
How to Apply

Applying for funding is simple.
You will first need to apply for a place on the course, using the
application form here
When your application has been accepted, you will automatically be
considered for a funded place if you meet the following criteria:
You have been made an unconditional offer for a place on the MSc. in Big
Data
You are a resident of Scotland or the EU (but NOT the rest of the UK)
We only have a limited number of funded places available, and they will be
allocated on a first come, first served basis.
This page is maintained by:
Computing Science and Mathematics
School of Natural Sciences
University of Stirling, Stirling FK9 4LA
Tel: +44 1786 46 7421

===================================================================================================================


2. Fully Funded Phd positions at Stirling on Search Based Software
Engineering http://www.cs.stir.ac.uk/~jrw/positions/positions.html


FUNDED PhD Positions

About DAASE

=========

DAASE is a four site project funded by the Engineering and Physical
Sciences Research Council involving University College London, Birmingham,
Stirling and York and with a growing list of industrial partners,
including: Berner and Mattner, BT Laboratories, Ericsson, GCHQ, Honda
Research Institute Europe,IBM,Microsoft Research and Motorola UK.

The project seeks to use Search Based Software Engineering to develop
optimised software development processes, combining aspects of software
engineering activities into a single combined and optimising process. This
new form of software engineering will be supported by the development and
evaluation of theory, algorithms and methods for advanced exact, meta and
hyper heuristic techniques. The goal is to produce software that is
dynamically adaptive; not only able to respond to and fix problems that
arise before deployment and during operation, but that continually
optimises, re-configures and evolves to adapt to new operating conditions,
platforms and environmental challenges (as most broadly construed).

DAASE will create an array of new processes, methods, techniques and tools
for a new kind of software engineering, radically transforming the theory
and practice of software engineering.

The Posts

=========

DAASE is a highly collaborative project. PhD students working on the
project will have at least one other "buddy partner site" (one of the four
academic partners specifically designated to collaborate) with which they
will collaborate, supported by visits to the partner site (of one to four
weeks duration), the full expenses of which will be met by the project.
PhDs will also have opportunities to visit and collaborate with industrial
and other partners and to be fully engaged with the international community
through conferences, workshops and other networking activities. This will
enhance training and development and open new opportunities for
collaboration and intellectual development.

A total of ten studentships are available.

Contact John R. Woodward [log in to unmask] http://www.cs.stir.ac.uk/~jrw/

Studentships will provide funding for tuition fees (home/EU rate), a
stipend of 13,590 per annum plus Research Training Support Grant of 750
pa.

Formal applications should be made via the online PG application form at
http://www.stir.ac.uk/postgraduate/research-degrees/school-of-natural-sciences/
Click the "apply now" button at the top right of the page. Select 'Research
Degree in Computing Science' and 'register as a new user' on the system to
proceed to the application form.

Source: www.stir.ac.uk/impact/

http://www.stir.ac.uk/postgraduate/research-degrees/school-of-natural-sciences/

Candidates who are from outside the European Union will be charged
international fee, which translates into a short-fall of around 8,000
GBP/year.

More information can be found in the following paper.

The EPSRC project website.

===================================================================================================================

3. 5th Workshop on Evolutionary Computation for the Automated Design of
Algorithms http://web.mst.edu/~tauritzd/ECADA/

5th Workshop on Evolutionary Computation for the Automated Design of
Algorithms

July 11-15, 2015 @ GECCO 2015 in Madrid, Spain

Description

How can we automatically generate algorithms on demand? While this was one
of the original aims of Machine Learning and Artificial Intelligence in the
early 1950s, and more recently Genetic Programming in the early 1990s,
existing techniques have fallen-short of this elusive goal. This workshop
will outline a number of steps in the right direction on the path to
achieving this goal. In particular, this workshop will focus on the
burgeoning field of hyper-heuristics which are meta-heuristics applied to a
space of algorithms; i.e., any method of sampling a set of candidate
algorithms. Genetic Programming has most famously been employed to this
end, but random search and iterative hill-climbing have both also
successfully been employed to automatically design novel (components of)
algorithms.

This approach is in contrast to standard Genetic Programming which attempts
to build programs from scratch from a typically small set of atomic
functions. Instead we take already existing programs and allow evolution to
improve on them. When the automatic design of algorithms is done using a
Genetic Programming system (effectively in vitro), the methodology is
typically referred to as Genetic Programming as a Hyper-heuristic. When the
automatic design of algorithms is done directly on source code (effectively
in situ), the methodology is typically referred to as Genetic Improvement
[5, 7].
Although most Evolutionary Computation techniques are designed to generate
specific solutions to a given instance of a problem, some of these
techniques can be explored to solve more generic problems. For instance,
while there are many examples of Evolutionary Algorithms for evolving
classification models in data mining and machine learning, the work
described in [1] employed a hyper-heuristic using Genetic Programming to
create a generic classification algorithm which will, in turn, generate a
specific classification model for any given classification dataset, in any
given application domain. In other words, the hyper-heuristic is operating
at a higher level of abstraction compared to how most search methodologies
are currently employed; i.e., we are searching the space of algorithms as
opposed to directly searching in the problem solution space [2], raising
the level of generality of the solutions produced by the hyper-heuristic
evolutionary algorithm. For instance, a hyper-heuristic can generate a
generic heuristic for solving any instance of the traveling salesman
problem, involving any number of cities and any set of distances associated
with those cities [3], whilst a conventional evolutionary algorithm would
just evolve a solution to one particular instance of the traveling salesman
problem, involving a predefined set of cities and associated distances
between them.

Hyper-heuristics have some distinctions from standard Genetic Programming.
In essence, they consist of a stage where an algorithmic framework or
template is defined along with algorithmic primitives, and another stage
where a meta-heuristic such as Genetic Programming searches the program
space defined by the aforementioned template and primitives. This approach
can be identified with the Template Method pattern from Designed Patterns
associated with Object Oriented programming. In short, the human provides
the overall architecture of the algorithm (e.g., loops and conditional
branching) and the meta-heuristic fills in the details (for example, the
bodies of the loops, or the condition and actions of conditional branching
statements). While this allows searches in constrained search spaces based
on problem knowledge, it does not in any way limit the generality of this
approach as the template can be chosen to be any executable program and the
primitive set can be selected to be Turing-complete. Typically, however,
the initial algorithmic primitive set is composed of primitive components
of existing high-performing algorithms for the problems being targeted;
this more targeted approach very significantly reduces the initial search
space, resulting in a practical approach rather than a mere theoretical
curiosity. Iterative refining of the primitives allows for gradual and
directed enlarging of the search space until convergence.

As meta-heuristics are themselves a type of algorithm, they too can be
automatically designed employing hyper-heuristics. For instance, in 2007,
genetic programming was used to evolve mate selection in evolutionary
algorithms [8]; in 2011, linear genetic programming was used to evolve
crossover operators [9]; more recently, genetic programming was used to
evolve complete black-box search algorithms [10,11].

The main objective of this workshop is to discuss hyper-heuristics
employing evolutionary computation methods for generating algorithms. These
methods have the advantage of producing solutions that are applicable to
any instance of a problem domain, instead of a solution specifically
produced to a single instance of the problem. The areas of application of
these methods include, for instance, data mining, machine learning, and
optimization [1-11].

[1] Gisele L. Pappa and Alex A. Freitas. Automating the Design of Data
Mining Algorithms: An Evolutionary Computation Approach, Springer, Natural
Computing Series, 2010.
[2] Edmund K. Burke, Matthew Hyde, Graham Kendall and John Woodward. A
genetic programming hyper-heuristic approach for evolving 2-D strip packing
heuristics. In IEEE Transactions on Evolutionary Computation,
14(6):942-958, December 2010.
[3] M. Oltean and D. Dumitrescu. Evolving TSP heuristics using multi
expression programming. In: Computational Science - ICCS 2004, Lecture
Notes in Computer Science 3037, pp. 670-673. Springer, 2004.
[4] John R. Woodward and Jerry Swan, "The automatic generation of mutation
operators for genetic algorithms", in Proceedings of the 14th international
conference on Genetic and evolutionary computation conference, 2012.
[5] William B. Langdon and Mark Harman. Genetically Improving 50000 Lines
of C++. Research Note , RN/12/09, Department of Computer Science,
University College London, Gower Street, London WC1E 6BT, UK, 2012.
[6] Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan.
Automatic Design of Scheduling Policies for Dynamic Multi-objective Job
Shop Scheduling via Cooperative Coevolution Genetic Programming. IEEE
Transactions on Evolutionary Computation, 18(2):193-208, April 2014.
[7] Justyna Petke, Mark Harman, William B. Langdon, and Westley Weimer.
Using Genetic Improvement & Code Transplants to Specialise a C++ Program to
a Problem Class Proceedings of the 17th European Conference on Genetic
Programming, EuroGP 2014, Granada, Spain, 2014. Springer Verlag.
[8] Ekaterina A. Smorodkina and Daniel R. Tauritz. Toward Automating EA
Configuration: the Parent Selection Stage. In Proceedings of CEC 2007 -
IEEE Congress on Evolutionary Computation, pages 63-70, Singapore,
September 25-28, 2007.
[9] Brian W. Goldman and Daniel R. Tauritz. Self-Configuring Crossover. In
Proceedings of the 13th Annual Conference Companion on Genetic and
Evolutionary Computation (GECCO '11), pages 575-582, Dublin, Ireland, July
12-16, 2011.
[10] Matthew A. Martin and Daniel R. Tauritz. Evolving Black-Box Search
Algorithms Employing Genetic Programming. In Proceedings of the 15th Annual
Conference Companion on Genetic and Evolutionary Computation (GECCO '13),
pages 1497-1504, Amsterdam, The Netherlands, July 6-10, 2013.
[11] Matthew A. Martin and Daniel R. Tauritz. A Problem Configuration Study
of the Robustness of a Black-Box Search Algorithm Hyper-Heuristic. In
Proceedings of the 16th Annual Conference Companion on Genetic and
Evolutionary Computation (GECCO '14), pages 1389-1396, Vancouver, BC,
Canada, July 12-16, 2014.
Call for Papers

This workshop explores the Automated Design of Algorithms employing
hyper-heuristics which are meta-heuristics applied to algorithm space, with
an emphasis on hyper-heuristics of the evolutionary computation persuasion.
Genetic Programming has most famously been employed to this end, but random
search and iterative hill-climbing have both also successfully been
employed to automatically design novel (components of) algorithms. These
methods have the advantage of producing solutions that are applicable to
any instance of a specified problem domain, instead of a solution
specifically produced for a single problem instance. This is particularly
useful for real-world problem solving where one can afford a large amount
of a priori computational time to subsequently solve many problem instances
drawn from a specified problem domain. The areas of application of these
methods include, for instance, data mining, machine learning, optimization,
bioinformatics, image processing, economics, cyber security, critical
infrastructure protection, etc. The workshop welcomes original submissions
on all aspects of Evolutionary Computation for the Automated Design of
Algorithms, which include - but are not limited to - the following topics
and themes:
Hyper-heuristics, in particular of the evolutionary computation persuasion,
for designing a particular type of algorithm/heuristic for anything from
optimization to machine learning to bioinformatics, etc.
Hyper-heuristics, in particular of the evolutionary computation persuasion,
for designing other algorithms of the evolutionary computation persuasion
(effectively Meta-Evolutionary Algorithms).
Empirical comparison of different hyper-heuristics.
Theoretical analyses of hyper-heuristics.
Automatic selection/creation of algorithm primitives (i.e., building
blocks) as a preprocessing step for the use of hyper-heuristics.
Analysis of the trade-off between generality and effectiveness of different
hyper-heuristics or of algorithms produced by a hyper-heuristic.
Analysis of the most effective representations for hyper-heuristics (e.g.,
Koza style Genetic Programming versus Cartesian Genetic Programming).
Real-world applications of hyper-heuristics.
Important Dates

Workshop paper submission deadline: April 3, 2015
Notification of acceptance: April 20, 2015
Camera-ready deadline: May 4, 2015
Registration deadline: May 4, 2015
Paper Submission

Submitted papers may not exceed 8 pages and are required to be in
compliance with the GECCO 2015 Call for Papers Preparation Instructions.
However, note that the review process of the workshop is not double-blind;
hence, authors' information should be included in the paper.
To submit, E-mail papers to both [log in to unmask] and [log in to unmask]

All accepted papers will be presented at the workshop and appear in the
GECCO Conference Companion Proceedings.

Workshop Schedule

This will be a quarter-day workshop. The schedule will be announced here
shortly after the paper acceptance notification deadline.
Organizers & Contact Info

E-mail: [log in to unmask]

Daniel R. Tauritz is an Associate Professor in the Department of Computer
Science at the Missouri University of Science and Technology (S&T), on
sabbatical at Sandia National Laboratories for the 2014-2015 academic year,
a former Guest Scientist at Los Alamos National Laboratory (LANL), the
founding director of S&T's Natural Computation Laboratory, and founding
academic director of the LANL/S&T Cyber Security Sciences Institute. He
received his Ph.D. in 2002 from Leiden University for Adaptive Information
Filtering employing a novel type of evolutionary algorithm. He served
previously as GECCO 2010 Late Breaking Papers Chair, COMPSAC 2011 Doctoral
Symposium Chair, GECCO 2012 GA Track Co-Chair, and GECCO 2013 GA Track
Co-Chair. For several years he has served on the GECCO GA track program
committee, the Congress on Evolutionary Computation program committee, and
a variety of other international conference program committees. His
research interests include the design of hyper-heuristics and
self-configuring evolutionary algorithms and the application of
computational intelligence techniques in cyber security, critical
infrastructure protection, and search-based software engineering. He was
granted a US patent for an artificially intelligent rule-based system to
assist teams in becoming more effective by improving the communication
process between team members.
E-mail: [log in to unmask]

John R. Woodward is a Lecturer at the University of Stirling, within the
CHORDS group and is employed on the DAASE project, and for the previous
four years was a lecturer with the University of Nottingham. He holds a BSc
in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer
Science, all from the University of Birmingham. His research interests
include Automated Software Engineering, particularly Search Based Software
Engineering, Artificial Intelligence/Machine Learning and in particular
Genetic Programming. He has over 50 publications in Computer Science,
Operations Research and Engineering which include both theoretical and
empirical contributions, and given over 100 talks at international
conferences and as an invited speaker at universities. He has worked in
industrial, military, educational and academic settings, and been employed
by EDS, CERN and RAF and three UK Universities.
Previous ECADA workshops

1st ECADA Worlkshop @GECCO 2011 - Dublin, Ireland
2nd ECADA Workshop @GECCO 2012 - Philadelphia, PA, USA
3rd ECADA Workshop @GECCO 2013 - Amsterdam, The Netherlands
4th ECADA Workshop @GECCO 2014 - Vancouver, BC, Canada


--thanks John R. Woodward (http://www.cs.stir.ac.uk/~jrw/)
--*see my calendar to suggest a meeting *
http://www.cs.stir.ac.uk/~jrw/calendar/calendar.html
--NEWS public lecture Cmprsd Vw f nfrmtn Thry: A Compressed View of
Information Theory, http://www.maths.stir.ac.uk/lectures/

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