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Efficient design of biological sequences will have a great impact across manyindustrial and healthcare domains. However, discovering improved sequencesrequires solving a difficult optimization problem. Traditionally, thischallenge was approached by biologists through a model-free method known as"directed evolution", the iterative process of random mutation and selection.As the ability to build models that capture the sequence-to-function mapimproves, such models can be used as oracles to screen sequences before runningexperiments. In recent years, interest in better algorithms that effectivelyuse such oracles to outperform model-free approaches has intensified. Thesespan from approaches based on Bayesian Optimization, to regularized generativemodels and adaptations of reinforcement learning. In this work, we implement anopen-source Fitness Landscape EXploration Sandbox (FLEXS:github.com/samsinai/FLEXS) environment to test and evaluate these algorithmsbased on their optimality, consistency, and robustness. Using FLEXS, we developan easy-to-implement, scalable, and robust evolutionary greedy algorithm(AdaLead). Despite its simplicity, we show that AdaLead is a remarkably strongbenchmark that out-competes more complex state of the art approaches in avariety of biologically motivated sequence design challenges.

AdaLead: A simple and robust adaptive greedy search algorithm for sequence design