An analysis of massively distributed evolutionary algorithms

Computational science is placing new demands on optimization algorithms as the size of data sets and the computational complexity of scientific models continue to increase. As these complex models have many local minima, evolutionary algorithms (EAs) are very useful for quickly finding optimal solutions in these challenging search spaces. In addition to the complex search spaces involved, calculating the objective function can be extremely demanding computationally. Because of this, distributed computation is a necessity. In order to address these computational demands, top-end distributed computing systems are surpassing hundreds of thousands of computing hosts; and as in the case of Internet based volunteer computing systems, they can also be highly heterogeneous and faulty. This work examines asynchronous strategies for distributed EAs using simulated computing environments. Results show that asynchronous EAs can scale to hundreds of thousands of computing hosts while being highly r...

• Type: Conference
• Year: 2010
• Where: CEC
• Authors: Travis J. Desell, David P. Anderson, Malik Magdon-Ismail, Heidi Jo Newberg, Boleslaw K. Szymanski, Carlos A. Varela
• Keywords: Artificial Intelligence, Asynchronous Eas, CEC 2010, Computing Environments, Distributed Computing