Daniilidis, A., Dominguez Corella, A., & Wissgott, P. (2025). Feature weighting for data analysis via evolutionary simulation. arXiv. https://doi.org/10.48550/arXiv.2511.06454
E105-04 - Forschungsbereich Variationsrechnung, Dynamische Systeme und Operations Research
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ArXiv ID:
2511.06454
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Date (published):
9-Nov-2025
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Number of Pages:
19
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Preprint Server:
arXiv
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Keywords:
feature weighting; evolutionary algorithms; multi-objective optimization; data analysis
en
Abstract:
We analyze an algorithm for assigning weights prior to scalarization in discrete multi-objective problems arising from data analysis. The algorithm evolves the weights (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. The method, originally inspired by evolutionary game theory, differs from standard weighting schemes in that it is analytically tractable with provable convergence.
