Titlebook: Estimation of Distribution Algorithms; A New Tool for Evolu Pedro Larra?aga,Jose A. Lozano Book 2002 Springer Science+Business Media New Yo

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https://doi.org/10.1007/978-94-011-6056-8ferent types of representation, three methods for obtaining the initial population and two different methods for handling the problem’s constraints. Experimental results for problems of different sizes are given.

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https://doi.org/10.1007/978-3-319-65912-1le 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 intro

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James R. Cullen,M. Melamud,K. HathawayEach individual obtained by simulation of the probability distribution learnt in each EDA generation represents a disjunction of a finite number of simple rules. This problem has been modeled to allow representations with different complexities. Experimental results comparing three types of EDAs —UM

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Crystalline Metal Oxide Catalystset of the network variables ., given some observations . This problem, also known as the ., is known to be NP-hard, so exact computation is not always possible. As partial abductive inference in Bayesian networks can be viewed as a combinatorial optimization problem, Genetic Algorithms have been suc