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Open AccessJournal ArticleDOI

An evolutionary algorithm for discrete tomography

Kees Joost Batenburg
- Vol. 151, Iss: 1, pp 36-54
TLDR
This paper presents an evolutionary algorithm for finding the reconstruction which maximises an evaluation function, representing the ''quality'' of the reconstruction, and shows that the algorithm can be successfully applied to a wide range of evaluation functions.
Abstract
One of the main problems in discrete tomography is the reconstruction of binary matrices from their projections in a small number of directions In this paper we consider a new algorithmic approach for reconstructing binary matrices from only two projections This problem is usually underdetermined and the number of solutions can be very large We present an evolutionary algorithm for finding the reconstruction which maximises an evaluation function, representing the ''quality'' of the reconstruction, and show that the algorithm can be successfully applied to a wide range of evaluation functions We discuss the necessity of a problem-specific representation and tailored search-operators for obtaining satisfactory results Our new search-operators can also be used in other discrete tomography algorithms

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Citations
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Book ChapterDOI

Combining Genetic Algorithm and Simulated Annealing Methods for Reconstructing HV-Convex Binary Matrices

TL;DR: This paper considers the discret tomography problem (DTP), namely reconstruction convex binary matrices from their row and column sums respectively H and V, RBM(H,V), reformulated as an integer programming problem.
Book ChapterDOI

Image Reconstruction from Projection under Periodicity Constraints Using Genetic Algorithm

TL;DR: This paper uses genetic algorithm to optimize the solution, which is an evolutionary technique to solve the problem of image reconstruction from a small number of projections.
Journal ArticleDOI

Discrete tomographic reconstruction via adaptive weighting of gradient descents

TL;DR: This work proposes a new method for multivalued DT, which performs the reconstruction as an energy minimisation task, and designs a novel optimisation process for approximating the minima of this energy function.
Book ChapterDOI

Generation and empirical investigation of hv-convex discrete sets

TL;DR: A method to generate some special hv-convex discrete sets from uniform random distribution is described and it is shown that the developed generation technique can easily be adapted to other classes of discrete sets, even for the whole class of hv -convexes.
Book ChapterDOI

A memetic island model for discrete tomography reconstruction

TL;DR: This work presents a combination of several instances of a recently proposed memetic algorithm for discrete tomography reconstruction, based on the island model parallel implementation, which is motivated by the fact that the results are finally better and more robust compared to other approaches.
References
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Book

Genetic Algorithms + Data Structures = Evolution Programs

TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Book

Network Flows: Theory, Algorithms, and Applications

TL;DR: In-depth, self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including descriptions of polynomial-time algorithms for these core models are presented.
Book

Tabu Search

TL;DR: This book explores the meta-heuristics approach called tabu search, which is dramatically changing the authors' ability to solve a host of problems that stretch over the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics, pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservation and scores of other problems.
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New Ideas In Optimization

TL;DR: The techniques treated in this text represent research as elucidated by the leaders in the field and are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search.
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Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing

TL;DR: Combinatorial Optimization and Boltzmann Machines, Parallel Simulated Annealing Algorithms, and Neural Computing.