Lattice reconfiguration vs. local selection criteria for diversity tuning in cellular GAs

This paper aims to compare the effect of dynamically controlling the exploration-exploitation trade-off in cellular Genetic Algorithms (cGAs) from two perspectives: first, through lattice reconfiguration while dynamically changing the grid-neighbourhood ratio and thus taking advantage of their inherent structural properties; second, through local selection using a recently developed method known as anisotropic selection which allows to modify the overall population selection pressure at a local level. For both perspectives, the dynamic control of selection pressure is implemented constantly (every n generations) or adaptively based on the loss of diversity at the phenotype or the genotype space. Benchmark problems ranging from academic to real and combinatorial problems have been tackled in order to fairly compare both approaches. Statistical significance tests have also been carried out to support the results herein presented.