A multi-population genetic algorithm approach for solving ill-posed problems

In this paper we develop a multi-population genetic algorithm (GA) for regularizing nonlinear ill-posed problems. Real coded genetic algorithms are used for calculating the minimizers of the Tikhonov functional. The algorithm is based on evolving separate populations for various values of the regularization parameter. The rate of convergence of the algorithm is substantially increased by exchanging information between neighbouring populations by the process of migration.