{
  "title": "PopulationSearch",
  "domain": "Active Inference",
  "type": "Paper",
  "creators": [
    {
      "name": "Nassim Dehouche"
    },
    {
      "name": "Daniel Friedman"
    }
  ],
  "description": "We propose integrating Active Inference into population-based metaheuristics to enhance performance through anticipatory environmental adaptation. Demonstrated with Ant Colony Optimization (ACO) on th...",
  "abstract": "We propose integrating Active Inference into population-based metaheuristics to enhance performance through anticipatory environmental adaptation. Demonstrated with Ant Colony Optimization (ACO) on the Travelling Salesman Problem (TSP), experimental results indicate Active Inference yields improved solutions with marginal increase in computational cost, with performance patterns relating to graph topology.",
  "keywords": [
    "population search",
    "Active Inference",
    "Ant Colony Optimization",
    "TSP",
    "metaheuristics",
    "anticipatory adaptation",
    "computational optimization"
  ],
  "methods": [
    {
      "name": "Free energy minimization",
      "description": "Applied free energy minimization approach"
    },
    {
      "name": "Bayesian modeling and inference",
      "description": "Applied bayesian modeling and inference approach"
    }
  ],
  "key_findings": [
    "We propose integrating Active Inference into population-based metaheuristics to enhance performance through anticipatory environmental adaptation..",
    "Demonstrated with Ant Colony Optimization (ACO) on th...."
  ],
  "related_papers": [
    "2018_WoodliceAndMen",
    "2020_BehaviorEngineering",
    "2021_ModelingConflict"
  ],
  "checked_at": "2026-07-01T20:50:01Z"
}