Title : Parameters of evolutionary algorithms and what to do about them
| Presenter | Guszti Eiben |
| Abstract | Finding appropriate parameter values for evolutionary algorithms (EA) is one of the persisting grand challenges of the evolutionary computing (EC) field. On the one hand, all EC researchers and practitioners acknowledge that good parameter values are essential for good EA performance. On the other hand, even after 30 years of EC research there is very little known about the effect of EA parameters on EA performance. Users mostly rely on conventions (mutation rate should be low), ad hoc choices (why not use uniform crossover), and experimental comparisons on a limited scale (testing combinations of three different crossover rates and three different mutation rates). Hence, there is a striking gap between the widely acknowledged importance of good parameter values and the widely exhibited ignorance concerning principled approaches to tune EA parameters. In this talk we discuss the parameter issue from various angles, outline options for on-line control or off-line tuning of parameters and look in more details into the benefits algorithmic approaches to parameter tuning. |
Title : When to stop? Cognitive science research on rules for stopping and switching tasks
| Presenter | Frank van Harmelen |
| Abstract | In the LarKC project, we are building the Large Knowledge Collider, an engine for infinitely scalable reasoning with very large data-set (think: web-scale). We plan to achieve this infinite scalabilty through parallelisation/distribution and through anytime/approximate reasoning. Both for distributed reasoning and for anytime reasoning, it is important to know when to stop (in particular when your dataset is too large to be handled completely anyway). Cognitive scientists are now helping us to find out which stopping rules people (and other animals) are using when they have to decide when to stop a task (or when to switch from one task to another). The hope is that they will uncover general stopping-heuristics that are not only effective for animals, but that can also be used for machine computations. In this talk I will outline the general ideas of this research, describe what is already known about stopping rules, and what there still is to be found out. |
| Slides | Click on that link to get the slides |