Mmao Cls Metabolic Multi Agent Optimization For Jo

# MMAO-Cls: Metabolic Multi-Agent Optimization for Joint Feature Selection and Classifier Tuning

Derived from arXiv:2607.01539 - MMAO-Cls: Metabolic Multi-Agent Optimization for Joint Feature Selection and Classifier Tuning

## Core Concept
This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can act as a credible outer-loop optimizer for classification model selection. We propose MMAO-Cls, a mixed-space realization in which each agent jointly encodes a binary feature mask and classifier hyperparameters, while private energy, communal budget, role drift, and lifecycle turnover are mapped to the accuracy-complexity tradeoff of wrapper learning. The implementation is strengthened by deriving feature-budget adaptation...

## Key Insights
- Derived from arXiv:2607.01539
- Published: 2026-07-01
- Utility Score: 0.97
- Authors: Jinliang Xu, Liping Ma

## Activation
mmao-cls-metabolic-multi-agent-optimization-for-jo, 2607.01539

## References
- arXiv: https://arxiv.org/abs/2607.01539