Optimization by Building and Using Probabilistic Models

Scope

Organized by: Mark Hauschild, Martin Pelikan and Kumara Sastry

Date: 13th of July, 2008

Location: Atlanta, Georgia, USA at GECCO 2008

Description: Genetic and evolutionary algorithms (GEAs) evolve a population of
candidate solutions using two main operators: (1) selection and (2)
variation. However, fixed, problem independent variation operators often
fail to effectively exploit important features of high quality solutions
obtained by selection to create novel, high-quality solutions. One way to
make variation operators more effective is to replace traditional
variation operators by the following two steps:

1. Estimate the distribution of the selected solutions on the basis of an
adequate probabilistic model.
2. Generate a new population of candidate solutions by sampling from the
distribution estimated.

Algorithms based on this principle are often called probabilistic
model-building genetic algorithms (PMBGAs), estimation of distribution
algorithms (EDAs) or iterated density estimation algorithms (IDEAs). The
purpose of this workshop is to discuss

recent advances in PMBGAs,

theoretical and empirical results,

applications of PMBGAs and

promising directions of future PMBGA research.