Cellular genetic algorithms without additional parameters

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs)
with decentralized population in which interactions among individuals are restricted
to close ones. The use of decentralized populations in GAs allows to keep the population
diversity for longer, usually resulting in a better exploration of the search space
and, therefore, in a better performance of the algorithm. However, it supposes the
need of several new parameters that have a major impact on the behavior of the algorithm.
In the case of cGAs, these parameters are the population and neighborhood
shapes. We propose in this work two innovative cGAs with new adaptive techniques
that allow removing the neighborhood and population shape from the algorithm’s
configuration. As a result, the new adaptive cGAs are highly competitive (statistically)
with all the compared cGAs in terms of the average solutions found in the
continuous and combinatorial domains, while finding, in general, the best solutions
for the considered problems, and with less computational effort.