# 🧠 Enhancing Population-based Search with Active Inference

**Nassim Dehouche, Daniel Friedman** (2024) · *ArXiv*

[![DOI](https://img.shields.io/badge/DOI-10.48550%2FarXiv.2408.09548-blue)](https://doi.org/10.48550/arXiv.2408.09548)

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## Abstract

> This paper proposes the integration of Active Inference into traditional population-based metaheuristics to enhance their performance through anticipatory environmental adaptation. Specifically, it demonstrates this approach using Ant Colony Optimization on the Travelling Salesman Problem, showing improved solutions with minimal computational cost increase.

## Keywords

`population search` · `Active Inference` · `Ant Colony Optimization` · `TSP` · `metaheuristics` · `anticipatory adaptation` · `computational optimization`

## Key Contributions

- Integration of Active Inference into population-based metaheuristics.
- Application of the proposed method to Ant Colony Optimization for the Travelling Salesman Problem.
- Demonstration of improved solution quality with marginal computational cost increase.
- Identification of performance patterns related to graph node number and topology.

## Methods

- Utilization of Active Inference framework for anticipatory adaptation.
- Experimental evaluation of Ant Colony Optimization with Active Inference.
- Analysis of performance based on varying graph structures.
- Comparison of traditional reactive approaches with the proposed anticipatory model.

## 🎯 Consulting & Tutoring

**Available for AI Research Consulting and Tutoring.** [Contact Daniel Ari Friedman, PhD](https://danielarifriedman.com/) for collaboration on Active Inference, Bayesian modeling, and computational biology.

## Citation

```bibtex
@article{2024_PopulationSearch,
  author = {Nassim Dehouche, Daniel Friedman},
  title = {{Enhancing Population-based Search with Active Inference}},
  journal = {ArXiv},
  year = {2024},
  doi = {10.48550/arXiv.2408.09548},
}
```

## File Inventory

- `AGENTS.md` (1,822 bytes)
- `2024_PopulationSearch.pdf` (487,730 bytes)
- `README.md` (1,557 bytes)
- `SKILL.md` (1,770 bytes)