EvoDOP-2007
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:
- identify the occurrence of a change in the environment and then
deliberately increase diversity in the population e.g. by
means of increased mutation
- try to avoid convergence all the time, e.g. by including
new random individuals in the population in every generation
- supply the EA with a memory, e.g. by using diploidy or an explicit
memory, so that the EA can recall useful information
from past generations.
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.
