VizGEC 2013 - International Workshop on Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2013)

Visualisation is a crucial tool in this area, and particular topics of interest are:
visualisation of the evolution of a synthetic genetic population
visualisation of algorithm operation
visualisation of problem landscapes
visualisation of multi-objective trade-off surfaces
the use of genetic and evolutionary techniques for visualising data
novel technologies for visualisation within genetic and evolutionary computation
facilitating human steer of algorithms
As well as allowing us to observe how individuals interact, visualising the evolution of a synthetic genetic population over time facilitates the analysis of how individuals change during evolution, allowing the observation of undesirable traits such as premature convergence and stagnation within the population. In addition to this, by visualising the problem landscape we can explore the distribution of solutions generated with a GEC method to ensure that the landscape has been fully explored. In the case of multi- and many-objective optimisation problems this is enhanced by the visualisation of the trade-off between objectives, a non-trivial task for problems comprising four or more objectives, in which it is necessary to provide an intuitive visualisation of the Pareto front to a decision maker. All of these areas draw together in the field of interactive evolutionary computation, in which it is vital that a decision maker be provided with as much information as they require to interact with the GEC method in the most efficient way possible, order to generate and understand good solutions quickly.
In addition to visualising the solutions generated by a GEC process, we can also visualise the processes themselves. It can be useful, for example, to investigate which evolutionary operators are most commonly applied by an algorithm, as well as how they are applied, in order to gain an understanding of how the process can be most effectively tuned to solve the problem at hand.
GEC methods have also recently been applied to the visualisation of data. As the amount of data available in areas such as bioinformatics increases rapidly, it is necessary to develop methods which can visualise large quantities of data; evolutionary methods can, and have, been used for this. Work on visualising the results of evolutionary data mining is also now appearing in the literature.
All of these methods benefit greatly from developments in high-powered graphics cards and work on 3D visualisation, largely driven by the computer games community. A workshop provides a good environment for the demonstration of such methods.
Based on these areas of interest the target audience for VizGEC is broad. We anticipate that people engaged in visualisation research will be interested, in addition to people from the GEC community who may be interested in using visualisation to advance their own work. We hope to attract both experienced practitioners as well as providing an introduction for those new to visualisation in GEC.