  he greatest weakness of evolutionary algorithms, widely used today, is the
premature convergence due to the loss of population diversity over generations.
To overcome this problem, several algorithms have been proposed, such as the
Graph-based Evolutionary Algorithm (GEA) \cite{1} which uses graphs to model
the structure of the population, but also memetic or differential evolution
algorithms \cite{2,3}, or diversity-based ones \cite{4,5} have been designed.
These algorithms are based on multi-populations, or often rather focus on the
self-tuning parameters, however, they become complex to tune because of their
high number of parameters. In this paper, our approach consists of an
evolutionary algorithm that allows a dynamic adaptation of the search operators
based on a graph in order to limit the loss of diversity and reduce the design
complexity.
