Elitistic Evolution: A Novel Micro-population Approach for Global Optimization Problems
Francisco Viveros-Jiménez, Efrén Mezura‐Montes, Alexander F. Gelbukh · 2009
Micro-population Evolutionary Algorithms (μ-EAs) are useful tools for optimization purposes. They can be used as optimizers for unconstrained, constraint and multi-objective problems. μ-EAs distinctive feature is the usage of very small populations. A novel μ-EA named Elitistic Evolution (EEv) is proposed in this paper. EEv is designed to solve high-dimensionality problems (N ≥ 30) without using complex mechanisms e.g. Hessian or covariance matrix. It is a simple heuristic that does not require a careful fine-tunning of its parameters. EEv principal features are: adaptive behavior and elitism. Its evolutionary operators: mutation, crossover and replacement, have the ability to search either locally (near a current point) or globally (on a distant point). This ability is controlled by a single adaptive parameter. EEv is tested on a set of well-known optimization problems and its performance is compared with respect to state-of-the-art algorithms, such as Differential Evolution, μ-PSO and Restart CMA-ES.