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  "Title": "Unified Simulation of Isomorphisms Between Ant Colony\nIntelligence and Machine Learning",
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  "Authors@R": "c(\nperson(\"Yuval\", \"Levental\", email = \"yhl3051@rit.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Gregory\", \"Babbitt\", email = \"gabsbi@rit.edu\", role = \"aut\"),\nperson(\"Ernest\", \"Fokoué\", email = \"epfeqa@rit.edu\", role = \"aut\"))",
  "Description": "Implements the full suite of simulation, visualization,\nand analysis tools for exploring the mathematical isomorphisms\nbetween ant colony decision-making and three major paradigms of\nmachine learning: random forests (Part I: variance reduction\nthrough decorrelation), boosting (Part II: bias reduction\nthrough adaptive recruitment), and neural networks (Part III:\ngradient-based generational learning). Accompanies the trilogy\n\"Isomorphic Functionalities between Ant Colony and Ensemble\nLearning\" (Fokoué, Babbitt, and Levental, 2026,\n<doi:10.48550/arXiv.2603.20328>,\n<doi:10.48550/arXiv.2604.00038>).",
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  "Encoding": "UTF-8",
  "URL": "https://github.com/ylevental/IsomorphismSim_Full",
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  "Date/Publication": "2026-04-16 19:21:34 UTC",
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      "title": "AntsNet: Unified Simulation of Ant Colony / Machine Learning Isomorphisms",
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