64062 workshops, 1 MSc, 7 FUNDED PhD positions + 1 tutorial
- Mar 7 5:49 AMApologies for cross-posting, please forward to interested parties.
2 workshops, 1 MSc in Big Data, 7 *funded* PhD positions, 1 tutorial.
1. Workshop on "The Automated Design of Algorithms" (see below)
2. Workshop on "Metaheuristic Design Patterns"
3. New BIG DATA MSc at Stirling www.cs.stir.ac.uk/entrants/bd
4. 7 *FUNDED* PhD positions
5. Tutorial on "The Automated Design of Algorithms"
4th Workshop on the Automated Design of Algorithms
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, Genetic Programming in the early
1990s, and more recently Genetic Improvement, 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.
This approach is in contrast to standard Genetic Programming which
attempts to build programs from scratch from a typically small set of
functions/instructions. Instead we take an already existing program(s)
and allow evolution to improve the program. When the automatic design
of algorithms is done using Genetic Programming (effectively "in
vitro"), the methodology is typically referred to as a Generative
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 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 examples of Evolutionary Algorithms for evolving
classification models in Data Mining or Machine Learning . In other
words, the Genetic Programming system is operating at a higher level
of abstraction compared to how most search methodologies are currently
employed, i.e. we are searching for solution methods, as opposed to
The automatic design of algorithms has some distinctions from standard
Genetic Programming. In essence, automatic design consists of a stage
where an algorithmic framework or template is defined and another
stage where Genetic Programming supplies candidate algorithms to plug
into the overall template. 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 (for example WHILE-loops, FOR-loops and
IF-THEN-ELSE statements) and genetic programming fills in the details
(for example, the bodies of the loops, or the condition and actions of
the IF-THEN-ELSE statements). Interestingly the resulting algorithm is
part man-made and part machine-made, and this distinguishes this
method from standard Genetic Programming.
Although the work in  consisted of evolving a complete data
mining/machine learning algorithm, in the area of optimization this
type of approach is named a hyper-heuristic. Hyper-heuristics are
search methods that automatically select and combine simpler
heuristics, creating a generic heuristic that is used to solve any
instance of a given target type of optimization problem. Hence,
hyper-heuristics search the space of heuristics, instead of directly
searching in the problem solution space , 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 ; 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.
Whether we name the approach for automatically designing algorithms or
hyper-heuristics, in both cases, a set of human-designed procedural
components or heuristics surveyed from the literature are chosen as a
starting point (or as "building blocks" or primitive components) for
the evolutionary search. Besides, new procedural components and
heuristics can be automatically generated, depending on which
components are first provided to the method.
The main objective of this workshop is to discuss methods for
automatically generating algorithms and/or hyper-heuristics. 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 may include, for instance, data
mining, machine learning, and optimization .
 G. L. Pappa and A. A. Freitas, Automating the Design of
Data Mining Algorithms: An Evolutionary Computation Approach,
Springer, Natural Computing Series, 2010. xiii + 187 pages.
 E. K. Burke, M. Hyde, G. Kendall and J. Woodward, A genetic
programming hyper-heuristic approach for evolving two
dimensional strip packing heuristics. In: IEEE Transactions on
Evolutionary Computation, 2010.
 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.
 John R. Woodward and Jerry Swan, "The automatic generation of
for genetic algorithms", in Proceedings of the 14th international conference on
Genetic and evolutionary computation conference, 2012.
 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,
 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. Accepted for future
 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
Paper submission deadline: March 28th
Notification of acceptance: April 15th
Camera-ready deadline: April 25th
Registration deadline: TBA
Submitted papers should follow the ACM format, and not exceed 8 pages.
Please see the GECCO 2014 information for authors for further details.
However, note that the review process of the workshop is not
double-blind. Hence, authors' information should appear in the paper.
All accepted papers will be presented at the workshop and appear in
the GECCO workshop volume. Proceedings of the workshop will be
published on CD-ROM, and distributed at the conference.
Papers should be submitted in PostScript or PDF format to: [jrw at cs
dot stir dot ac dot uk], and contain the subject "GECCO 2014
Workshop". A conformation of recipt email will be sent by return
This will be a half-day workshop. Each presentation is planned to last
for 20 minutes followed by 10 minutes for discussions, and the panel
will last 45 minutes.
John Woodward - University of Stirling, United Kingdom
Jerry Swan - University of Stirling, United Kingdom
Earl Barr - University College London, United Kingdom
John Woodward - jrw at cs dot stir dot ac dot uk
Jerry Swan - jsw at cs dot stir dot ac dot uk