Differential Evolution (DE) stochastic algorithms for global
optimization of problems with and without constraints.
The aim is to curate a collection of its state-of-the-art variants that
(1) do not sacrifice simplicity of design,
(2) are essentially tuning-free, and
(3) can be efficiently implemented directly in the R language.
Currently, it only provides an implementation of the 'jDE' algorithm by
Brest et al. (2006)
CHANGES IN DEoptimR VERSION 1.0-0
First release of DEoptimR: implementation of a variant of the jDE algorithm.
Constraint handling based on biasing feasible over unfeasible solutions by a parameter-less variant of the epsilon-constrained method.