AntsNet

AntsNet is a comprehensive R package for exploring the mathematical isomorphisms between ant colony decision-making and machine learning. It unifies three previously separate packages into a single toolkit accompanying the research trilogy:

Part	arXiv	Isomorphism
I	2603.20328	Random Forests ≅ Ant Colonies (variance reduction via decorrelation)
II	2604.00038	Boosting ≅ Adaptive Recruitment (bias reduction via sequential reweighting)
III	2604.09677	Neural Networks ≅ Generational Colony Learning (gradient descent via pheromone evolution)

Installation

# From CRAN (when available)
install.packages("AntsNet")

# Development version from GitHub
remotes::install_github("ylevental/IsomorphismSim_Full")

Quick Start

Part I: Random Forest ≅ Ant Colony

library(AntsNet)

# Simulate an ant colony
sim <- simulate_ant_colony(n_ants = 50, p_explore = 0.3)
cat("Colony chose site", sim$decision, "\n")

# Run variance decomposition experiment
rf_results <- variance_decomposition_experiment(n_replicates = 20)
plot_variance_decomposition(rf_results)

# Direct isomorphism test
iso <- isomorphism_test(n_replicates = 20)
plot_correlation_decay(iso)

Part II: Boosting ≅ Adaptive Recruitment

# AdaBoost
data <- generate_classification_data(n = 200, p = 5, noise = 0.1)
boost_res <- adaboost(data$X, data$y, M = 50)

# Ant Colony Adaptive Recruitment
sq <- c(10, 8, 6, 4, 2)
acar_res <- acar(sq, n_ants = 30, n_waves = 50)

# Compare weight/pheromone evolution
plot_weight_pheromone(boost_res, acar_res, sq)

# Weak learnability experiment
wl <- weak_learnability_experiment(n_replicates = 50)
plot_weak_learnability(wl)

Part III: Neural Network ≅ Generational Colony Learning

# Compare gradient descent and generational pheromone learning
plot_isomorphism(site_qualities = c(10, 7, 5, 4, 3), n_generations = 50)

# Learning curves side by side
plot_learning_curves(n_replicates = 10, n_generations = 50)

# Neural plasticity ↔ colony adaptation
plot_plasticity(n_generations = 100, env_shift_at = 50)

Interactive Exploration

# Shiny app for Part I (decorrelation explorer)
launch_app("part1")

# Shiny app for Part II (boosting explorer)
launch_app("part2")

Package Structure

Module	Functions	Isomorphism
part1_*	simulate_ant_colony(), variance_decomposition_experiment(), isomorphism_test(), ...	RF ≅ Colony
part2_*	adaboost(), acar(), weak_learnability_experiment(), ...	Boosting ≅ ACAR
part3_*	gacl(), simple_neural_network(), plot_isomorphism(), ...	NN ≅ GACL

Renamed functions (from the original separate packages)

To avoid name collisions in the unified package:

Original (separate package)	Unified (AntsNet)	Reason
generate_data() (Part I)	generate_regression_data()	Different signatures
generate_data() (Part II)	generate_classification_data()	Different signatures
plot_noise_robustness() (Part II)	plot_noise_robustness_boost()	Name collision with Part III
plot_noise_robustness() (Part III)	plot_noise_robustness_nn()	Name collision with Part II
plot_convergence() (Part II)	plot_convergence_boost()	Clarity
noise_experiment() (Part II)	noise_experiment_boost()	Clarity
convergence_experiment() (Part II)	convergence_experiment_boost()	Clarity

The Isomorphism in One Equation

All three isomorphisms share the same underlying principle. For an ensemble of N units with individual variance σ² and pairwise correlation ρ:

Var[ensemble] = ρσ² + (1 − ρ)σ²/N

This holds identically for:

• Random forests: trees decorrelated by random feature selection (θ = m_try/p)
• Ant colonies: ants decorrelated by stochastic exploration (θ = p_explore)

The ant colony is a random forest running on biological hardware; the random forest is an ant colony running on silicon.

Citation

@misc{fokoue2026partI,
  title={Decorrelation, Diversity, and Emergent Intelligence: The Isomorphism
         Between Social Insect Colonies and Ensemble Machine Learning},
  author={Fokou{\'e}, Ernest and Babbitt, Gregory and Levental, Yuval},
  year={2026},
  eprint={2603.20328},
  archivePrefix={arXiv},
  primaryClass={stat.ML}
}

@misc{fokoue2026partII,
  title={Isomorphic Functionalities between Ant Colony and Ensemble Learning:
         Part II --- On the Strength of Weak Learnability and the Boosting Paradigm},
  author={Fokou{\'e}, Ernest and Babbitt, Gregory and Levental, Yuval},
  year={2026},
  eprint={2604.00038},
  archivePrefix={arXiv},
  primaryClass={stat.ML}
}

Authors

• Yuval Levental (maintainer) — Center for Imaging Science, RIT
• Gregory Babbitt — Gosnell School of Life Sciences, RIT
• Ernest Fokoué — School of Mathematics and Statistics, RIT

License

MIT