For more details on my stay in Trento (seminar times and locations etc.), contact Luigi Marengo.
A measure of search difficulty, fitness distance correlation (FDC), will be introduced and examined in relation to genetic algorithm (GA) performance. In many cases, this correlation can be used to predict the performance of a GA on problems with known global maxima. It correctly classifies easy deceptive problems as easy and difficult non-deceptive problems as difficult, indicates when Gray coding will prove better than binary coding, and is consistent with the surprises encountered when GAs were used on the Tanese and royal road functions. The FDC measure is a consequence of an investigation into the connection between GAs and heuristic search.
A major reason for the maintenance of a population in a Genetic Algorithm (GA) is the hope of increased performance via direct communication of information between individuals. This communication is achieved through the use of a crossover operator. If crossover is not a useful method for this exchange, the GA may not, on average, perform any better than a variety of simpler algorithms that are not population-based. I will present a simple method for testing the usefulness of crossover for a particular problem instance. This allows the identification of situations in which crossover is apparently useful but is actually only producing gains that could be obtained, or exceeded, with macromutation and no population.
I will give an overview of Echo, present research results on patterns of species abundance in Echo, and discuss important issues in the modeling of complex adaptive systems.