Workshop date: Sunday, June 26, 14:00 - 18:00

Workshop topic:

Genetic and evolutionary algorithms (GEAs) evolve a population of candidate solutions to a given optimization problem using two basic operators: (1) selection and (2) variation. Selection introduces a pressure toward high-quality solutions, whereas variation ensures exploration of the space of all potential solutions. Two variation operators are common in current genetic and evolutionary computation (GEC): (1) crossover, and (2) mutation. Crossover creates new candidate solutions by combining bits and pieces of promising solutions, whereas mutation introduces slight perturbations to promising solutions to explore their immediate neighborhood. However, fixed, problem independent variation operators often fail to effectively exploit important features of high-quality solutions obtained by selection. One way to make variation operators more powerful and flexible is to replace traditional variation of GEAs by the following two steps:

1. Build a probabilistic model of the selected promising solutions, and
2. sample the built model to generate a new population of candidate solutions.

Algorithms based on this principle are 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 present and discuss

- recent advances in PMBGAs,
- new theoretical and empirical results,
- applications of PMBGAs, and
- promising directions for future PMBGA research.

Attendance:

Attendance to the workshop is open to all GECCO-2005 attendees. GECCO-2005 is operated by ISGEC, the International Society for Genetic and Evolutionary Computation, Inc., a not-for-profit corporation. The conference is administered by the American Association for Artificial Intelligence, 445 Burgess Drive, Menlo Park, CA 94025 USA. Phone: 650-328-3123. Fax: 650-321-4457. For more information on GECCO-2005: GECCO 2005 Homepage