Leveraging diversity in evolutionary algorithms using a population injection method
Robin Mueller-Bady, Martin Kappes, Inmaculada Medina‐Bulo, Francisco Palomo‐Lozano · 2016
In this paper, we present a new, computationally inexpensive method for preventing premature convergence in multimodal evolutionary algorithms by population injection. Our method avoids the premature convergence of the population around one or multiple local optima by maintaining an adequate amount of genetic diversity. The technique does not require any setup or maintenance effort during runtime as is the case for other proposed techniques addressing the same issue (e.g., island, cellular, or diffusion model EAs as population models or specific operators for increasing genetic diversity in mutation and recombination). We present experimental results comparing a (μ, λ) EA using our method, which has been named population injection evolutionary algorithm (PI-EA), against cellular EA and classical (μ,λ) EA for some standard benchmark functions. In the results it can be observed that applying population injection improves the results produced by (μ, λ) EAs for all benchmarks under consideration, in one case even up to 59%.