Assigns fitness scores to each mutant based on a multi-objective function, e.g.:
Fitness=w1f(Gbind)+w2f(kcat/Km)+w3Stopo\text{Fitness} = w_1 \cdot f(\Delta G_{\text{bind}}) + w_2 \cdot f(k_{\text{cat}}/K_m) + w_3 \cdot S_{\text{topo}}
where StopoS_{\text{topo}} is a score based on structural robustness or novelty.
d. Selection and Feedback Module
Simulates natural and artificial selection: Tournament-based selection. Probabilistic fitness-proportional selection. Multi-agent reinforcement learning
Successful sequences feed back into the mutation generator via adaptive mutation probabilities, representing context-sensitive mutational pressure.
3. CAS-Specific Dynamics Implementation
The engine incorporates key CAS behaviors:
Emergent attractors: Clusters of highly functional mutants may emerge as localized attractors in genotype space.
Bifurcation detection: The system tracks topological shifts in folding networks to detect critical transitions.
Self-modification: Modules can update internal thresholds (e.g., entropy cutoffs, mutation rates) based on macro-performance over epochs.
