An Island Strategy for Memetic Discrete Tomography Reconstruction

Abstract

In this paper we present a parallel island model memetic algorithm for binary discrete tomography reconstruction that uses only four projections without any further a priori information. The underlying combination strategy consists in separated populations of agents that evolve by means of different processes. Agents progress towards a possible solution by using genetic operators, switch and a particular compactness operator. A guided migration scheme is applied to select suitable migrants by considering both their own and their sub-population fitness. That is, from time to time, we allow some individuals to transfer to different subpopulations. The benefits of this paradigm were tested in terms of correctness, robustness and time of the reconstruction by considering publicly available datasets of images. To tackle the so-called stability problem, we considered the case of noisy projections along four directions to simulate an instrumental error. Results show that the proposed method decreases the reconstruction error for all classes of images with respect to a serial implementation recently proposed by the authors, and that such reconstruction error is almost invariant with respect to the number of demes. Moreover, the computation time of the proposed parallel memetic algorithm scales in a quasi-linear manner with respect to the demes number, and is invariant with respect to the used number of migrations.