Towards a parameterless out-of-the-box population size control for evolutionary and swarm-based algorithms for single objective bound constrained real-parameter numerical optimization

We present an innovative step towards a parameterless out-of-the-box population size control for evolutionary and swarm-based algorithms for single objective bound constrained real-parameter numerical optimization. To the best of our knowledge, our approach is the first parameterless out-of-the-box parameter control for such a kind of technique. It is easy to implement and to use, since it does not require the adjustment of any parameter. The general idea is to increment the velocity of the population change if the best fitness stagnates, and decrement it otherwise. Then, in order to effectively change the population size, a mechanism of removal/addition of individuals inspired by the selection methods of evolutionary algorithms is executed. Our experimental results provide evidence that our controller is not only compatible with any evolutionary or swarm-based algorithm for single objective bound constrained real-parameter numerical optimization, but that it also performs well in many scenarios. •This paper proposes a parameterless out-of-the-box population size control method for monoobjective evolutionary and swarm-based algorithms.•The absence of parameters makes such a method straightforward to be used. Besides, its simple rationale makes it easy to implement.•The proposed method is lightweight, which means that it usually does not add any significant overhead to the optimization process.•The proposed controller is suitable to be implemented with any evolutionary and swarm-based algorithm.