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The k-means clustering problem is a famous problem with a variety of applications. It can be summarized as finding the best k representative centers for an input data set. K-means algorithm and its variations are known to be fast approximation iterative algorithms to the problem. However, several studies have shown that the genetic algorithm (GA) performs more favorably. In this paper, a new crossover...
An improved genetic algorithm (IGA) is presented for geophysical P-wave amplitude variation with offset (AVO) inversion. The optimization method includes two phases: a new real coded GA with hybrid selection operator and hybrid crossover operator serves as a base level search. It makes a quick decision to direct the search towards the optimal region. In phase-2, the diversity of the population is...
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