Multi Niche Parallel GP with a Junk-Code Migration Model

We describe in this paper a parallel implementation of Multi Niche Genetic Programming that we use to test the performance of a modified migration model. Evolutive introns is a technique developed to accelerate the convergence of GP in classification and symbolic regression problems. Here, we will copy into a differentiated subpopulation the individuals that due to the evolution process contain longer Evolutive Introns. Additionally, the multi island model is parallelised in order to speed up convergence. These results are also analysed. Our results prove that the multi island model achieves faster convergence in the three different symbolic regression problems tested, and that the junk-coded subpopulation is not significantly worse than the others, which reinforces our belief in that the important thing is not only fitness but keeping good genetic diversity along all the evolution process. The overhead introduced in the process by the existence of various island, and the migration model is reduced using a multi-thread approach.