منابع مشابه
RGFGA: An Efficient Representation and Crossover for Grouping Genetic Algorithms
There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree. We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resultin...
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The main purpose of this paper is to solve an inverse random differential equation problem using evolutionary algorithms. Particle Swarm Algorithm and Genetic Algorithm are two algorithms that are used in this paper. In this paper, we solve the inverse problem by solving the inverse random differential equation using Crank-Nicholson's method. Then, using the particle swarm optimization algorith...
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− Two near-optimum planarization algorithms are presented. The algorithms belong to a general class of algorithms known as genetic algorithms because the search procedures on which they are based are inspired on the mechanics of natural selection and natural genetics. The first algorithm is intended to generate a near-maximal planar subgraph from a given graph and also provides the routing info...
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Anonymization techniques that provide k-anonymity suffer from loss of quality when the data dimensionality is high. Microaggregation techniques are not an exception. Given a set of records, attributes are grouped into non-intersecting subsets and microaggregated independently. While this improves quality by reducing the loss of information, it usually leads to the loss of the k-anonymity proper...
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ژورنال
عنوان ژورنال: Software: Practice and Experience
سال: 1998
ISSN: 0038-0644,1097-024X
DOI: 10.1002/(sici)1097-024x(199808)28:10<1137::aid-spe192>3.0.co;2-4