yesterday in the evening, the gecco keynote took place at the national history museum in london.
a great location, especially for meeting “some of the greatest minds in evolutionary biology” (gecco program preamble, Hod Lipson): Richard Dawkins, Lewis Wolpert and Steve Jones.

Here are some pictures (sorry for some of them being blurred, couldn’t dare using a flashlight ):

the location:

inside:

the panel:

Hopefully, some of you have better quality images that they are willing to share.

as jj proposed on his blog, some of the guys blogging about the gecco 2007 could meet up somewhere in london.
sounds like a good idea!

great city, can’t state that often enough.
i was just wandering around yesterday evening, here are some impressions:

covent garden:

trafalgar square/nelson’s column (where the tour de france started lately, for the first time in london):

big ben:

and finally, the location where gecco 2007 takes place, the University College London:

now i’m going to prepare for my presentation which is scheduled for tuesday, five o’clock.
though… no need to hurry, perhaps i’ll take a sunbath at hyde park during lunch break first.

Today, i submitted the final revision of our paper entitled “Self-Adaptive Ant Colony Optimisation Applied to Function Allocation in Vehicle Networks”, which i am going to present at the GECCO 2007 [1] in London (July 7-11, 2007).

Here’s the abstract:

Modern vehicles possess an increasing number of software and hardware components that are integrated in electronic control units (ECUs). Finding an optimal allocation for all components is a multi-objective optimisation problem, since every valid allocation can be rated according to multiple objectives like costs, busload, weight, etc. Additionally, several constraints mainly regarding the availability of resources have to be considered. This paper introduces a new variant of the well-known ant colony optimisation, which has been applied to the real-world problem described above. Since it concerns a multi-objective optimisation problem, multiple ant colonies are employed. In the course of this work, pheromone updating strategies specialised on constraint handling are developed. To reduce the effort needed to adapt the algorithm to the optimisation problem by tuning strategic parameters, self-adaptive mechanisms are established for most of them. Besides the reduction of the effort, this step also improves the algorithm’s convergence behaviour.

Feel free to contact me to get the full paper.

[1] gecco 2007