  Heterogeneous Parallel Island Models (HePIMs) run different bio-inspired
algorithms (BAs) in their islands. From a variety of communication topologies
and migration policies fine-tuned for homogeneous PIMs (HoPIMs), which run the
same BA in all their islands, previous work introduced HePIMs that provided
competitive quality solutions regarding the best-adapted BA in HoPIMs. This
work goes a step forward, maintaining the population diversity provided by
HePIMs, and increasing their flexibility, allowing BA reconfiguration on
islands during execution: according to their performance, islands may
substitute their BAs dynamically during the evolutionary process. Experiments
with the introduced architectures (RecHePIMs) were applied to the NP-hard
problem of sorting permutations by reversals, using four different BAs, namely,
simple Genetic Algorithm, Double-point crossover Genetic Algorithm,
Differential Evolution, and self-adjusting Particle Swarm Optimization. The
results showed that the new reconfigurable heterogeneous models compute better
quality solutions than the HePIMs closing the gap with the HoPIM running the
best-adapted BA.
