Additionally, phase-space visualization allows researchers to map:
Genotypic diversity vs. functional performance
Stability transitions over successive generations
Mutation-path clustering and convergence
4. Interfacing with Machine Learning
To accelerate and refine the process, the simulation engine is designed to interface seamlessly with machine learning systems such as:
Graph Neural Networks for structural prediction and property estimation
Reinforcement Learning agents that adapt mutation strategies based on past outcomes
Variational Autoencoders (VAEs) for compressing the search space of viable enzyme architectures
This hybrid CAS-ML fusion allows the system to balance exploratory search (diversity generation) with exploitative focus (performance refinement)---a critical trade-off in both natural and synthetic evolution.
5. Output and Interpretability
