# A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems

arXiv: https://arxiv.org/abs/2512.05971 · HTML: https://mlsanity.com/paper/2512.05971/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes](https://mlsanity.com/paper/2008.12987/) (2020)
- [Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review](https://mlsanity.com/paper/2312.16174/) (2023)
- [An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring](https://mlsanity.com/paper/2012.06830/) (2020)

## Built on by (most cited first, 2 of 2)

- [CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing](https://mlsanity.com/paper/2603.06361/) (2026)
- [Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing](https://mlsanity.com/paper/2605.16433/) (2026)

## Abstract (arXiv)

The integration of advanced technologies, such as Artificial Intelligence (AI), into manufacturing processes is attracting significant attention, paving the way for the development of intelligent systems that enhance efficiency and automation. This paper uses the term "Gentelligent system" to refer to systems that incorporate inherent component information (akin to genes in bioinformatics-where manufacturing operations are likened to chromosomes in this study) and automated mechanisms. By implementing reliable fault detection methods, manufacturers can achieve several benefits, including improved product quality, increased yield, and reduced production costs. To support these objectives, we propose a hybrid framework with a dominance-based multi-objective evolutionary algorithm. This mechanism enables simultaneous optimization of feature selection and classification performance by exploring Pareto-optimal solutions in a single run. This solution helps monitor various manufacturing operations, addressing a range of conflicting objectives that need to be minimized together. Manufacturers can leverage such predictive methods and better adapt to emerging trends. To strengthen the validation of our model, we incorporate two real-world datasets from different industrial domains. The results on both datasets demonstrate the generalizability and effectiveness of our approach.