Scaling in distributed evolutionary algorithms with persistent population

Abstract

This work presents the experimental results obtained with a distributed computing system created by mapping an evolutionary algorithm to the CouchDB object store. The framework decouples the population from the evolutionary algorithm and -through the API that CouchDB provides- allows the distributed and asynchronous operation of clients written in different programming languages. In this paper we present tests which prove that the novel algorithm design still performs as good as a canonical evolutionary algorithm and discover what are the main issues concerning it, what kind of speedups should we expect, and how all this affects the fundamental evolutionary algorithms concepts.