Adrian Horia Dediu; Lars Hildenbrand; Claudio Moraga
A Remote Objects Implementation of Distributed Evolutionary Algorithms for Performances Analysis

Abstract.
We designed and implemented a new Distributed Evolutionary Algorithms architecture based on remote objects. For experiments we used a relatively large test function set starting with simple problems and continuing with more complicated test problems with many local optima. First we did a very large number of experiments for a simple test problem and then we randomly selected samples consisting in 100 experiments. We observed that the average number of evaluations until the solution was found for the sample tests differ no more than 4% by the average number of evaluations for the whole test set. We tested the behavior of Evolutionary Algorithms running in similar conditions in centralized mode, distributed isolated evolutions with stop message between sub-populations and distributed with individual exchange. The results of the tests for maximizing the threePeaksExt function showed that Distributed Evolutionary Algorithms perform better than centralized Evolutionary Algorithms, mainly due to the fact that premature convergence observed in centralized Evolutionary Algorithms is delayed due to individuals exchanges between sub-populations.