Continuous Parameter Pools in Ensemble Self-Adaptive Differential Evolution

Ensemble of parameters and mutation strategies differential evolution (EPSDE) is an elegant, promising optimization framework recently introduced in the literature. The idea behind it is that a pool of mutation and crossover strategies, along with associated pools of parameters, can flexibly adapt to a large variety of problems when a simple success based rule is introduced. Modern versions of this scheme attempts to improve upon the original performance at the cost of a high complexity. One of most successful implementations of this algorithmic scheme is the Self-adaptive Ensemble of Parameters and Strategies Differential Evolution (SaEPSDE). This paper operates on the SaEPSDE, reducing its complexity by identifying some algorithmic components that we experimentally show as possibly unnecessary. The result of this de-constructing operation is a novel algorithm implementation, here referred to as j Ensemble of Strategies Differential Evolution (jESDE). The proposed implementation is drastically simpler than SaEPSDE as several parts of it have been removed or simplified. Nonetheless, jESDE appears to display a competitive performance, on diverse problems throughout various dimensionality values, with respect to the original EPSDE algorithm, as well as to SaEPSDE and three modern algorithms based on Differential Evolution.