Information-Based Evolution of Boolean Networks

Biological evolution can be viewed as the acquisition and use of information under physical constraints. We examine this perspective through statistical physics and information theory, asking how information-based selection metrics shape the dynamics and architecture of evolving systems. Using computational experiments on Random Boolean Networks (RBNs) with information-driven fitness functions (e.g., mutual-information–based measures), we find that selection for information systematically yields networks with simpler attractor structure—shorter cycles and fewer attractors—than neutral or randomly evolved controls. A group-theoretic symmetry analysis further classifies Boolean functions into symmetry-defined families, revealing organizing principles that evolutionary search preferentially exploits. Introducing additional regulatory connections induces symmetry breaking within these families, offering a plausible mechanism for the emergence of complexity and innovation. Finally, we propose an analytical Evolutionary Dynamics equation that accurately reproduces evolutionary trajectories observed in simulations of small RBNs. Together, these results show how information-guided selection, constrained and structured by symmetry, organizes regulatory logic and tunes the dynamical repertoire of gene-regulatory-style networks.