A differential evolution strategy
Dariusz Jagodziński, Jarosław Arabas · 2017
This contribution introduces an evolutionary algorithm (EA) for continuous optimization in ℝn. The algorithm generates new individuals by the standard nonelitist truncation selection and the differential mutation to generate new individuals. The differential mutation is enriched by adding a random vector in the direction of the shift of population midpoint. Difference vectors are generated with the use of the archive of previous populations. Boundary constraints are handled by penalty function.