Feature weighting for data analysis via evolutionary simulation

We analyze an algorithm for assigning weights prior to scalarization in discrete multi-objective problems arising from data analysis.

The algorithm evolves weights (interpreted as the relevance of features) by a replicator-type dynamic on the standard simplex, with update indices computed from a normalized data matrix.

We prove that the resulting sequence converges globally to a unique interior equilibrium, yielding non-degenerate limiting weights.