# Population-Based Black-Box Optimization for Biological Sequence Design

arXiv: https://arxiv.org/abs/2006.03227 · HTML: https://mlsanity.com/paper/2006.03227/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 29 of 29)

- [Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders](https://mlsanity.com/paper/2203.12742/) (2022)
- [Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization](https://mlsanity.com/paper/2202.08450/) (2022)
- [Conservative Objective Models for Effective Offline Model-Based Optimization](https://mlsanity.com/paper/2107.06882/) (2021)
- [Data-Driven Offline Decision-Making via Invariant Representation Learning](https://mlsanity.com/paper/2211.11349/) (2022)
- [Data-Driven Offline Optimization For Architecting Hardware Accelerators](https://mlsanity.com/paper/2110.11346/) (2021)
- [Position: Leverage Foundational Models for Black-Box Optimization](https://mlsanity.com/paper/2405.03547/) (2024)
- [Apollo: Transferable Architecture Exploration](https://mlsanity.com/paper/2102.01723/) (2021)
- [Bayesian Optimization over Permutation Spaces](https://mlsanity.com/paper/2112.01049/) (2021)
- [Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC](https://mlsanity.com/paper/2212.09925/) (2022)
- [Adaptive machine learning for protein engineering](https://mlsanity.com/paper/2106.05466/) (2021)
- [Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2406.08799/) (2024)
- [Unifying Likelihood-free Inference with Black-box Optimization and Beyond](https://mlsanity.com/paper/2110.03372/) (2021)
- [Generative Adversarial Model-Based Optimization via Source Critic Regularization](https://mlsanity.com/paper/2402.06532/) (2024)
- [Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks](https://mlsanity.com/paper/2410.22296/) (2024)
- ["Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood](https://mlsanity.com/paper/2405.16730/) (2024)
- [Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms](https://mlsanity.com/paper/2407.00236/) (2024)
- [Extrapolative Controlled Sequence Generation via Iterative Refinement](https://mlsanity.com/paper/2303.04562/) (2023)
- [Offline-to-online hyperparameter transfer for stochastic bandits](https://mlsanity.com/paper/2501.02926/) (2025)
- [Parallelizing Contextual Bandits](https://mlsanity.com/paper/2105.10590/) (2021)
- [Fourier Representations for Black-Box Optimization over Categorical Variables](https://mlsanity.com/paper/2202.03712/) (2022)
- [Forecasting labels under distribution-shift for machine-guided sequence design](https://mlsanity.com/paper/2211.10422/) (2022)
- [Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design](https://mlsanity.com/paper/2409.18582/) (2024)
- [Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking](https://mlsanity.com/paper/2502.17246/) (2025)
- [Is novelty predictable?](https://mlsanity.com/paper/2306.00872/) (2023)
- [Curriculum-Augmented GFlowNets For mRNA Sequence Generation](https://mlsanity.com/paper/2510.03811/) (2025)
- [Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization](https://mlsanity.com/paper/2602.00737/) (2026)
- [BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design](https://mlsanity.com/paper/2409.16298/) (2024)
- [Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability](https://mlsanity.com/paper/2410.14573/) (2024)
- [Semantically Rich Local Dataset Generation for Explainable AI in Genomics](https://mlsanity.com/paper/2407.02984/) (2024)

## Abstract (arXiv)

The use of black-box optimization for the design of new biological sequences is an emerging research area with potentially revolutionary impact. The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences--a setting that off-the-shelf black-box optimization methods are ill-equipped to handle. We find that the performance of existing methods varies drastically across optimization tasks, posing a significant obstacle to real-world applications. To improve robustness, we propose Population-Based Black-Box Optimization (P3BO), which generates batches of sequences by sampling from an ensemble of methods. The number of sequences sampled from any method is proportional to the quality of sequences it previously proposed, allowing P3BO to combine the strengths of individual methods while hedging against their innate brittleness. Adapting the hyper-parameters of each of the methods online using evolutionary optimization further improves performance. Through extensive experiments on in-silico optimization tasks, we show that P3BO outperforms any single method in its population, proposing higher quality sequences as well as more diverse batches. As such, P3BO and Adaptive-P3BO are a crucial step towards deploying ML to real-world sequence design.