TY - GEN
T1 - A distributed pool architecture for genetic algorithms
AU - Roy, Gautam
AU - Lee, Hyunyoung
AU - Welch, Jennifer L.
AU - Zhao, Yuan
AU - Pandey, Vijitashwa
AU - Thurston, Deborah
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - The genetic algorithm (GA) paradigm is a wellknown heuristic for solving many problems in science and engineering. As problem sizes increase, a natural question is how to exploit advances in distributed and parallel computing to speed up the execution of GAs. This paper proposes a new distributed architecture for GAs, based on distributed storage of the individuals in a persistent pool. Processors extract individuals from the pool in order to perform the computations and then insert the resulting individuals back into the pool. Unlike previously proposed approaches, the new approach is tailored for distributed systems in which processors are loosely coupled, failure-prone and can run at different speeds. Proofof- concept simulation results are presented indicating that the approach can deliver improved performance due to the distribution and tolerates a large fraction of crash failures.
AB - The genetic algorithm (GA) paradigm is a wellknown heuristic for solving many problems in science and engineering. As problem sizes increase, a natural question is how to exploit advances in distributed and parallel computing to speed up the execution of GAs. This paper proposes a new distributed architecture for GAs, based on distributed storage of the individuals in a persistent pool. Processors extract individuals from the pool in order to perform the computations and then insert the resulting individuals back into the pool. Unlike previously proposed approaches, the new approach is tailored for distributed systems in which processors are loosely coupled, failure-prone and can run at different speeds. Proofof- concept simulation results are presented indicating that the approach can deliver improved performance due to the distribution and tolerates a large fraction of crash failures.
UR - http://www.scopus.com/inward/record.url?scp=70449767994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449767994&partnerID=8YFLogxK
U2 - 10.1109/CEC.2009.4983079
DO - 10.1109/CEC.2009.4983079
M3 - Conference contribution
AN - SCOPUS:70449767994
SN - 9781424429592
T3 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009
SP - 1177
EP - 1184
BT - 2009 IEEE Congress on Evolutionary Computation, CEC 2009
T2 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009
Y2 - 18 May 2009 through 21 May 2009
ER -