TY - GEN
T1 - A study on the configuration of migratory flows in island model differential evolution
AU - Lopes, Rodolfo Ayala
AU - Silva, Rodrigo C. Pedrosa
AU - Freitas, Alan R.R.
AU - Campelo, Felipe
AU - Guimarães, Frederico G.
PY - 2014/7/12
Y1 - 2014/7/12
N2 - The Island Model (IM) is a well known multi-population approach for Evolutionary Algorithms (EAs). One of the critical parameters for defining a suitable IM is the migration topology. Basically it determines the Migratory Flows (MF) between the islands of the model which are able to improve the rate and pace of convergence observed in the EAs coupled with IMs. Although, it is possible to find a wide number of approaches for the configuration of MFs, there still is a lack of knowledge about the real performance of these approaches in the IM. In order to fill this gap, this paper presents a thorough experimental analysis of the approaches coupled with the state-of-the-art EA Differential Evolution. The experiments on well known benchmark functions show that there is a trade-off between convergence speed and convergence rate among the different approaches. With respect to the computational times, the results indicate that the increase in implementation complexity does not necessarily represent an increase in the overall execution time.
AB - The Island Model (IM) is a well known multi-population approach for Evolutionary Algorithms (EAs). One of the critical parameters for defining a suitable IM is the migration topology. Basically it determines the Migratory Flows (MF) between the islands of the model which are able to improve the rate and pace of convergence observed in the EAs coupled with IMs. Although, it is possible to find a wide number of approaches for the configuration of MFs, there still is a lack of knowledge about the real performance of these approaches in the IM. In order to fill this gap, this paper presents a thorough experimental analysis of the approaches coupled with the state-of-the-art EA Differential Evolution. The experiments on well known benchmark functions show that there is a trade-off between convergence speed and convergence rate among the different approaches. With respect to the computational times, the results indicate that the increase in implementation complexity does not necessarily represent an increase in the overall execution time.
UR - https://dl.acm.org/citation.cfm?id=2605439
U2 - 10.1145/2598394.2605439
DO - 10.1145/2598394.2605439
M3 - Conference publication
SN - 9781450328814
SP - 1015
EP - 1022
BT - Proceedings of the 2014 Conference on Genetic and Evolutionary Computation - GECCO'14
PB - ACM
ER -