Title: DESA: Differential Evolution-Simulated
Combinations of Gaussians to Data
Annealing
for
Fitting
Linear
Author: Rizavel Corsino-Addawe
Degree: Master of Science in Statistics
Abstract:
The method of Differential Evolution (DE) is applied to the problem of fitting linear
combinations of Gaussians to data. The DE method offers advantages over competing
methods such as the Simulated Annealing (SA) algorithm. Firstly, the DE does not
require an auxiliary special method to obtain initial parameter estimates as in SA
algorithm. Secondly, based on experimental computer runs, the DE method is faster and
gives more accurate results than those obtaines by SA.
This paper also illustrates the combination of the search capabilities of DE and SA to
develop a hybrid algorithm, DESA, which is equally applicable and has better searching
ability and power to reach a near optimal solution.