# Feature weighting for data analysis via evolutionary simulation

arXiv: https://arxiv.org/abs/2511.06454 · HTML: https://mlsanity.com/paper/2511.06454/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 9 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [A survey on feature weighting based K-Means algorithms](https://mlsanity.com/paper/1601.03483/) (2015)
- [Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization](https://mlsanity.com/paper/2501.19113/) (2025)

## Built on by (most cited first, 1 of 1)

- [Bias Robustness of Genetic Multi-Criteria Sorting versus Learned Rankers under Proxy Skew and Label Contamination](https://mlsanity.com/paper/2610.14015/) (2026)

## Abstract (arXiv)

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.