AgentEvolve: LLM-Guided Multi-Objective Evolutionary Optimization for Expensive Black-Box Design Problems

Many engineering workflows rest on an expensive multi-objective design problem: a designer sets a few named parameters, runs a slow tool to score each candidate, and trades off conflicting objectives such as the area, speed and power of a chip, within a budget of tens to hundreds of evaluations. Tuning chipimplementation flows, sizing analog circuits and searching neural architectures are such problems. Their parameters have names, units and a short description that tell an expert what good designs look like. Evolutionary and Bayesian optimizers ignore this information; language-model optimizers use it but fix the model’s role in advance, and budget-matched studies often trace their improvements to the surrounding loop rather than the model. We propose AGENTEVOLVE, a drop-in replacement for evolutionary optimizers in which the model is one more source of candidate designs and earns its budget share by measurement: the initial population it writes, periodic proposals and a sampler program it authors are pooled with evolutionary offspring, and each source receives evaluations in proportion to the hypervolume its past candidates added to the front. We evaluate one configuration on nine benchmarks (OpenROAD, an open-source flow that turns a hardware design into a chip layout, on three chip technologies; the NAS-Bench-301 architecture-search benchmark; XGBoost hyperparameter tuning; a knapsack; the sizing of two analog circuits) at validation seeds kept apart from development: AGENTEVOLVE needs 3.09× fewer evaluations than the evolutionary optimizer NSGA-II to reach a target hypervolume on NAS-Bench-301 and 2.90× fewer on a low-dropout regulator, ending with 28% and 50% more hypervolume, and outperforms AGENTEVOLVE without LLM on three benchmarks. On the knapsack a program the model writes from the item descriptions brings the median run within 10^−4 of the exact front’s hypervolume, using the cheapest model tier at under $0.33 per run.