Title:On the Performance of Master-Slave Parallelization Methods for Multi-Objective Evolutionary AlgorithmsAuthor(s):Ciprian Zavoianu, Edwin Lughofer, Werner Koppelstätter, Günther Weidenholzer, Wolfgang Amrhein, Erich KlementAbstract:This paper is focused on a comparative analysis of the performance of two master-slave parallelization methods, the basic generational scheme and the steady-state asynchronous scheme. Both can be used to improve the convergence speed of multi-objective evolutionary algorithms (MOEAs) that rely on time-intensive fitness evaluation functions. The importance of this work stems from the fact that a correct choice for one or the other parallelization method can lead to considerable speed improvements with regards to the overall duration of the optimization. Our main aim is to provide practitioners of MOEAs with a simple but effective method of deciding which master-slave parallelization option is better when dealing with a time-constrained optimization process.Booktitle:Artificial Intelligence and Soft ComputingPublisher:Springer Berlin HeidelbergEditor(s):Laszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. ZuradaISBN:978-3-642-38609-1Page Reference:page 122-134, 622 page(s)Publishing:6/2013Series:Lecture Notes in Artificial Intelligence (LNAI)Volume:7895