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).