Electrical Impedance Tomography
SIAM Review
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Statistical inverse problems: discretization, model reduction and inverse crimes
Journal of Computational and Applied Mathematics - Special issue: Applied computational inverse problems
A genetic algorithm approach to image reconstruction in electricalimpedance tomography
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
In this article, various metaheuristics for a numerical optimization problem with application to Electric Impedance Tomography are tested and compared. The experimental setup is composed of a real valued Genetic Algorithm, the Differential Evolution, a self adaptive Differential Evolution recently proposed in literature, and two novel Memetic Algorithms designed for the problem under study. The two proposed algorithms employ different algorithmic philosophies in the field of Memetic Computing. The first algorithm integrates a local search into the operations of the offspring generation, while the second algorithm applies a local search to individuals already generated in the spirit of life-time learning. Numerical results show that the fitness landscape and difficulty of the optimization problem heavily depends on the geometrical configuration, as well the proposed Memetic Algorithms seem to be more promising when the geometrical conditions make the problem harder to solve.