Parallel Island Model Genetic Algorithms applied in NP-Hard problems

Designing efficient parallel island Genetic Algorithms (GA) is a difficult task: several decisions are needed related to the adequate structure of the islands, how they are connected, how many individuals should migrate, and how often they should migrate. The impact of these choices has not yet been fully understood since they might vary for different problems. In previous work, a variety of island model GAs to solve Reversal Distance Problem (RDP) over uni-crhomosomal genomes were proposed from which adequate choices were pointed out that provided results with an excellent balance among accuracy and performance. In this work, another evolutionary problem is considered in order to analyze how general were the decisions taken for island model GAs over RDP. The problem is translocation distance over multi-chromosomal genomes, which involves the interchange of gene between different chromosomes. Despite the fact that this problem falls also in the category of evolutionary distance problems, it is different from the RDP. Regarding accuracy, island models using a dynamic communication topology for exchange of individuals between islands provided the best solutions; while regarding performance, models using a static topology reached the highest speedup. Comparing with previous work on RDP, it was observed that islands models that did not provided good accuracy in RDP provided good quality solutions for translocation distance problem, while the best island models for RDP did not repeat the same success for translocation distance problem. The only invariant is that all the island model GAs in addition to competitive speedups provided better results than the corresponding sequential GA.