The objective function (or fitness function) is the measure that evaluates the optimality of the generated evolutionary algorithm solutions in the search space. In clustering domain, the fitness function refers to the adequacy of the partitioning. Accordingly, it needs to be formulated carefully, taken into consideration that the clustering is an unsupervised process.
Different objective functions generate different solutions even form the same evolutionary algorithm. Presuming also that the fitness could either be a minimization or a maximization function. Moreover, the algorithm could be formulated with one or with multi objective functions. To sum up, "choosing optimizati...
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