Seminars in Trento

For more details on my stay in Trento (seminar times and locations etc.), contact Luigi Marengo.

Summary of Lecture Topics

  1. Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms (Thursday April 11)
  2. Crossover, Macromutation and Population-based Search (Thursday April 11)
  3. An Introduction to Echo (Friday April 12)

Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms

The material for this lecture will be based directly on a paper I presented at the 6th International Conference on Genetic Algorithms (July 1995). Here is a postscript copy of the paper.

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.

Crossover, Macromutation and Population-based Search

The material for this lecture will be based directly on a paper I presented at the 6th International Conference on Genetic Algorithms (July 1995). Here is a postscript copy of the paper.

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.

An Introduction to Echo

Echo is a simulation tool developed to investigate mechanisms which regulate diversity and information-processing in systems comprised of many interacting adaptive agents, or complex adaptive systems (CAS). Echo agents interact via combat, mating and trade to develop strategies for ensuring survival in resource-limited environments. Individual genotypes are encodings of rules for interactions. In a typical simulation, populations of these genomes evolve complicated networks of interactions and resource flows. Resulting networks may be thought of as resembling species communities in ecological systems. Flexibly defined parameters and initial conditions enable researchers to conduct a range of what-if experiments.

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.

Terry Jones (terry <AT> jon.es)