Cost-Normalized Constrained Surrogate Search for a Fed-Batch Model: Reported Sequential and Batch Comparisons

Abstract

Sequential surrogate search can help allocate limited evaluations of a constrained bioprocess model. This study reports SURA, a cost-normalized feasible-improvement heuristic with greedy batches and a training-feasibility audit. The numerical record compares five methods on a deterministic four-variable fed-batch surrogate and two constrained analytic problems, using 150 evaluations and ten seeds per configured method–problem pair. On the bioprocess problem, SURA’s best feasible endpoint titer is 18.804 ± 0.176 g/L, compared with 19.152 ± 0.193 g/L for sequential GP-EI and 14.678 ± 2.244 g/L for budget-matched random search. The reported signed-rank comparisons favor GP-EI over SURA and SURA over random search (Holm p = 0.0078 for each). Disabling batching gives a reported finite-reference difference of −1.172 ± 0.191 g/L versus −0.756 ± 0.176 for the full method, but aggregate ablations do not uniquely identify model staleness as the cause. The audit did not trigger, and its fallback was not validated. The implemented kernel is RBF plus a white-noise term; there are no biological interaction contexts, assay-noise observations or cross-program portfolio decisions in these tests. Budgets are evaluation counts rather than equal cumulative costs. Negative reference differences indicate exceeding a finite random-search sample, not a certified optimum. Original simulator, random-stream, acquisition and plot-generation records remain necessary for reproducibility verification.