In this paper, we present a computationally inexpensive method for maintaining genetic diversity in evolutionary algorithms using population injection. As opposed to other methods, e.g., cellular EAs, population injection does not require any maintenance or setup effort.   Here, we present first experimental results comparing a (μ, λ) EA with and without population injection and a cellular EA using the h1 benchmark. As can be observed in the results, population injection is worth to be considered for problems which suffer from premature convergence.
['Robin Mueller-Bady', 'Martin Kappes', 'Inmaculada Medina-Bulo', 'Francisco Palomo-Lozano']
Maintaining Genetic Diversity in Multimodal Evolutionary Algorithms using Population Injection