Neural clustering analysis of macroevolutionary and genetic algorithms in the evolution of robot controllers

In this work, we will use self-organizing feature maps as a method of visualization the sampling of the fitness space considered by the populations of two evolutionary methods, genetic and macroevolutionary algorithms, in a case with a mostly flat fitness landscape and low populations. Macroevolutionary algorithms will allow obtaining better results due to the way in which they handle the exploration-exploitation equilibrium. We test it with different alternatives using the self-organizing maps.