  This paper presents the Anisotropic selection scheme for cellular Genetic
Algorithms (cGA). This new scheme allows to enhance diversity and to control
the selective pressure which are two important issues in Genetic Algorithms,
especially when trying to solve difficult optimization problems. Varying the
anisotropic degree of selection allows swapping from a cellular to an island
model of parallel genetic algorithm. Measures of performances and diversity
have been performed on one well-known problem: the Quadratic Assignment Problem
which is known to be difficult to optimize. Experiences show that, tuning the
anisotropic degree, we can find the accurate trade-off between cGA and island
models to optimize performances of parallel evolutionary algorithms. This
trade-off can be interpreted as the suitable degree of migration among
subpopulations in a parallel Genetic Algorithm.
