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Feature weighting is an aspect of increasing importance in clustering because data are becoming more and more complex. In this paper, we propose new feature weighting methods based on genetic algorithms. These methods use the cost function defined in LKM as a fitness function. We present new methods based on Darwinian, Lamarckian, and Baldwinian evolution. For each one of them, we describe evolutionary and coevolutionary versions. We compare classical hill-climbing optimization with these six genetic algorithms on different datasets. The results show that the proposed methods, except Darwinian methods, are always better than the LKM algorithm.