A differential evolution method used to minimize functions of real variables. Evolution strategies are significantly faster
at numerical optimization than traditional genetic
algorithms and also more likely to find a function's true global
extremum.

These methods heuristically "mimic" biological evolution: namely, the process of natural selection and the "survival of the fittest" principle. An adaptive
search procedure based on a "population" of candidate solution points is
used. Iterations involve a competitive selection that drops the poorer solutions.
The remaining pool of candidates with higher "fitness value" are then "recombined"
with other solutions by swapping components with another; they can also be "mutated"
by making some smaller-scale change to a candidate. The recombination and mutation
moves are applied sequentially; their aim is to generate new solutions that are biased
towards subsets of
in which good, although not necessarily globally optimized, solutions have already
been found. Numerous variants of this general strategy based on diverse evolution
"game rules" can be constructed. The different types of evolutionary search
methods include approaches that are aimed at continuous global
optimization problems, and also others that are targeted towards solving combinatorial
problems. The latter group is often called genetic algorithms (Goldberg 1989, Michalewicz
1996, Osman and Kelly 1996, Voss et al. 1999).