Benchmarking the differential evolution with adaptive encoding on noiseless functions
Petr Pošík, Václav Klemš · 2012
The differential evolution (DE) algorithm is equipped with the recently proposed adaptive encoding (AE) which makes the algorithm rotationally invariant. The resulting algorithm, DEAE, should exhibit better performance on non-separable functions. The aim of this article is to assess what benefits the AE has, and what effect it has for other function groups. DEAE is compared against pure DE, an adaptive version of DE (JADE), and an evolutionary strategy with covariance matrix adaptation (CMA-ES). The results suggest that AE indeed improves the performance of DE, particularly on the group of unimodal non-separable functions, but the adaptation of parameters used in JADE is more profitable on average. The use of AE inside JADE is envisioned.