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 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.

• Publication year

  2025

• Venue

  Unknown venue

• Publication date

  2025-11-09

• Fields of study

  Mathematics, Computer Science

• Identifiers
  arXiv 2511.06454
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization

  2025cited by this paper
• A Survey of Weight Vector Adjustment Methods for Decomposition-Based Multiobjective Evolutionary Algorithms

  2020cited by this paper
• A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

  2016cited by this paper
• A Survey on Feature Weighting Based K-Means Algorithms

  2015cited by this paper
• MOEA/D with Adaptive Weight Adjustment

  2014cited by this paper
• Natural selection. IV. The Price equation *

  2012cited by this paper
• The Extended Phenotype: The Long Reach of the Gene

  2008cited by this paper
• MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition

  2007cited by this paper
• ZIB Structure Prediction Pipeline: Composing a Complex Biological Workflow Through Web Services

  2006cited by this paper
• The selfish gene, 30th anniversary ed.

  2006cited by this paper
• Genetic Algorithms for Feature Weighting: Evolution vs. Coevolution and Darwin vs. Lamarck

  2005cited by this paper
• Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

  2003cited by this paper
• A fast and elitist multiobjective genetic algorithm: NSGA-II

  2002cited by this paper
• The Physiology of the Grid An Open Grid Services Architecture for Distributed Systems Integration

  2002cited by this paper
• Grid information services for distributed resource sharing

  2001cited by this paper
• SPEA2: Improving the strength pareto evolutionary algorithm

  2001cited by this paper
• The GRID: Blueprint for a New Computing Infrastructure

  2000cited by this paper
• Feature Subset Selection by Bayesian network-based optimization

  2000cited by this paper
• Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

  2000cited by this paper
• Estimating Attributes: Analysis and Extensions of RELIEF

  1994cited by this paper
• Inductive Reasoning and Bounded Rationality

  1994cited by this paper
• A Practical Approach to Feature Selection

  1992cited by this paper
• Identification of common molecular subsequences.

  1981cited by this paper

• No citing papers are available for this paper.