Abstract
Graph matching has become an important area of research because of the potential advantages of using graphs for solving recognition problems. An example of its use is in image recognition problems, where structures to be recognized are represented by nodes in a graph that are matched against a model, which is also represented as a graph.
As the number of image recognition areas that make use of graphs is increasing, new techniques are being introduced in the literature. Graph matching can also be regarded as a combinatorial optimization problem with constraints and can be solved with evolutionary computation techniques such as Estimation of Distribution Algorithms.
This chapter introduces for the first time the use of Estimation of Distribution Algorithms with individuals represented as permutations to solve a particular graph matching problem. This is illustrated with the real problem of recognizing human brain images.
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Bengoetxea, E., Larrañaga, P., Perchant, A., Bloch, I. (2002). Solving Graph Matching with EDAs Using a Permutation-Based Representation. In: Larrañaga, P., Lozano, J.A. (eds) Estimation of Distribution Algorithms. Genetic Algorithms and Evolutionary Computation, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1539-5_12
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DOI: https://doi.org/10.1007/978-1-4615-1539-5_12
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