Island model in ActoDatA: an actor-based implementation of a classical distributed evolutionary computation paradigm

In this paper, we make a first assessment of the performance of ActoDatA, a novel actor-based software library for distributed data analysis and machine learning in Java that we have recently developed. To do so we have implemented an evolutionary machine learning application based on a distributed island model. The model implementation is compared to an equivalent implementation in ECJ, a popular general-purpose evolutionary computation library that provides support for distributed computing. The testbed used for comparing the two distributed versions has been an application of Sub-machine code Genetic Programming to the design of efficient low-resolution binary image classifiers. The results we have obtained show that the ActoDatA implementation is more efficient than the corresponding ECJ implementation.