Fitness and Overfitness: Implicit Regularization in Evolutionary Dynamics

This repository contains code to reproduce simulations and generate plots from the manuscript:

Fitness and Overfitness: Implicit Regularization in Evolutionary Dynamics
Hagai Rappeport and Mor Nitzan

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Overview

This project implements a computational framework to study the evolution of biological complexity through the lens of implicit regularization in evolutionary dynamics. It leverages the mathematical analogy between the replicator equation and Bayesian inference to explore how organismal complexity evolves to match environmental complexity.

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Repository Contents

• bayesian_evolution.py
  Contains the full implementation of the evolutionary simulation framework, including:
  • Definition of various genotype-to-phenotype mappings of different complexity (currently supported linear functions, polynomials and neural networks)
  • Environmental complexity modeling
  • Replicator dynamics simulation
  • Fitness calculations
  • Plotting routines to generate figures in the style of those in the paper

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Requirements

• Python 3.8+
• NumPy
• Matplotlib
• SciPy

License

This project is released under the MIT License. See LICENSE for details.

Contact

For questions or collaboration inquiries, please contact:

Hagai Rappeport — [hagai.rappeport@huji.mail.ac.il] Mor Nitzan — [mor.nitzan@huji.mail.ac.il]