  We propose and analyse a variant of the recently introduced kinetic based
optimization method that incorporates ideas like survival-of-the-fittest and
mutation strategies well-known from genetic algorithms. Thus, we provide a
first attempt to reach out from the class of consensus/kinetic-based algorithms
towards genetic metaheuristics. Different generations of genetic algorithms are
represented via two species identified with different labels, binary
interactions are prescribed on the particle level and then we derive a
mean-field approximation in order to analyse the method in terms of
convergence. Numerical results underline the feasibility of the approach and
show in particular that the genetic dynamics allows to improve the efficiency,
of this class of global optimization methods in terms of computational cost.
