Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
SETI@home: an experiment in public-resource computing
Communications of the ACM
Distributed and Persistent Evolutionary Algorithms: A Design Pattern
Proceedings of the Second European Workshop on Genetic Programming
Browser-based distributed evolutionary computation: performance and scaling behavior
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (1143 - 1198) " Distributed Bioinspired Algorithms"; Guest editors: F. Fernández de Vega, E. Cantú-Paz
Data-intensive computing for competent genetic algorithms: a pilot study using meandre
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A distributed pool architecture for genetic algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A-Teams and Their Applications
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Resilience to churn of a peer-to-peer evolutionary algorithm
International Journal of High Performance Systems Architecture
CouchDB: The Definitive Guide Time to Relax
CouchDB: The Definitive Guide Time to Relax
Implementation matters: programming best practices for evolutionary algorithms
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Pool-Based distributed evolutionary algorithms using an object database
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Hi-index | 0.00 |
This paper studies SofEA, an architecture for distributing evolutionary algorithms (EAs) across computer networks in an asynchronous and decentralized way. SofEA is based on a pool architecture which is implemented using an object store interacting asynchronously with several clients. The fact that each client is autonomous leads to a complex behavior that will be examined in this paper, so that the design can be validated, rules of thumb can be extracted and the limits of scalability found. We will show how, beyond the usual measures employed in EA, specific measures such as the number of conflicts across clients can give us hints on the algorithm behavior, and how implementation details can change not only the running time, but also the behavior of the evolutionary algorithm itself. By using these measures we try to find ideal values for parameters such as the simultaneous number of individuals evaluated by a client or the way these are chosen from the pool.