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.

翻译：我们提出并分析了一种近期提出的基于动力学的优化方法的变体，该方法融合了遗传算法中众所周知的适者生存和变异策略等思想。因此，我们首次尝试将共识/动力学类算法与遗传元启发式算法联系起来。遗传算法的不同代次通过具有不同标签的两个物种来表征，在粒子级别规定二元相互作用，然后我们推导出平均场近似，以便从收敛性角度分析该方法。数值结果证实了该方法的可行性，并特别表明遗传动力学能够从计算成本的角度提高此类全局优化方法的效率。