Optimisation problems are of prime importance in scientific and engineering communities. Many day-to-day tasks in these fields can be classified as optimisation problems. Due to their enormous solution spaces, optimisation problems frequently lie in class NP. In such cases, engineers and researchers have to rely on algorithms and techniques that can find sub-optimal solutions to these problems. One of the most dependable algorithms for numerical optimisation problems is Differential Evolution (DE). Since its introduction in the mid 1990’s, DE has been on the fore front when it comes to applicability of optimisation algorithms to variety of real-parameter optimisation problems. This popularity of DE has driven intensive research to further improve its capability to find optimal solutions. In this thesis we present a variant of DE to produce improved solutions with greater reliability. In doing so, we introduce a novel strategy to incorporate ancestral vectors into the optimisation process. We show that a controlled introduction of ancestral vectors into the optimisation process has a generally positive influence on convergence rate of the algorithm. Evaluation of the proposed algorithm forms a major part of this work, as an empirical evidence serves to demonstrate the performance of stochastic algorithms. The resulting implementation of the algorithm is made available as an open source software along with its reference manual.