Feature weighting for data analysis via evolutionary simulation
Optimization and Control
2026-05-08 v2 Machine Learning
Abstract
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.
Cite
@article{arxiv.2511.06454,
title = {Feature weighting for data analysis via evolutionary simulation},
author = {Aris Daniilidis and Alberto Domínguez Corella and Philipp Wissgott},
journal= {arXiv preprint arXiv:2511.06454},
year = {2026}
}