Evolutionary Algorithms for Dynamic Optimization Problems

Organized by: Peter A.N. Bosman and Jürgen Branke

Workshop as part of the Genetic and Evolutionary Computation Conference (GECCO-2007)
on July 07-11, 2007 (Saturday - Wednesday), London, England

Workshop date: July 08, 2007

The workshop was successfully held at GECCO-2007
Visit the "Program" section to download pdf presentations

Many real-world optimization problems are dynamic. New jobs are to be added to a schedule, the quality of the raw material may be changing, new orders have to be included into the routing of a fleet of vehicles, etc. In such cases, when the problem changes over the course of the optimization, the purpose of the optimization algorithm changes from finding an optimal solution to being able to continuously track the movement of the optimum through time. Since in a sense natural evolution is a process of continuous adaptation, it seems straightforward to consider evolutionary algorithms as appropriate candidates for dynamic optimization problems.

And indeed, several attempts have been made to modify evolutionary algorithms, to tune them for optimization in a changing environment. It was observed in all these studies, that the dynamic environment requires the evolutionary algorithm to maintain sufficient diversity for a continuous adaptation to the changes of the landscape. The following basic strategies for modifying the evolutionary algorithm can be identified:

More recent developments in the area include the use of anticipation, the role of flexibility, and multi-criteria aspects.

The goal of this workshop is to foster interest in the important subject of evolutionary algorithms for dynamic optimization problems, get together the researchers working on this topic, and to discuss recent trends in the area.