Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
New ideas in optimization
Principles of computerized tomographic imaging
Principles of computerized tomographic imaging
Generation and reconstruction of hv-convex 8-connected discrete sets
Acta Cybernetica
Stability and instability in discrete tomography
Digital and image geometry
PGAC: A Parallel Genetic Algorithm for Data Clustering
CAMP '05 Proceedings of the Seventh International Workshop on Computer Architecture for Machine Perception
An introduction to periodical discrete sets from a tomographical perspective
Theoretical Computer Science
A genetic algorithm for discrete tomography reconstruction
Genetic Programming and Evolvable Machines
A distributed genetic algorithm for restoration of vertical line scratches
Parallel Computing
An Evolutionary Approach for Object-Based Image Reconstruction Using Learnt Priors
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
An evolutionary algorithm for discrete tomography
Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
Reconstruction of 8-connected but not 4-connected hv-convex discrete sets
Discrete Applied Mathematics - Special issue: Advances in discrete geometry and topology (DGCI 2003)
A memetic algorithm for binary image reconstruction
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
A memetic approach to discrete tomography from noisy projections
Pattern Recognition
A memetic island model for discrete tomography reconstruction
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
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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.