This chapter introduces evolutionary algorithm-based (EA-based) symbolic regression, which is an optimization model inspired by nature. EA-based symbolic regression is used to predict reading comprehension proficiency by using English learners' vocabulary and grammatical knowledge. EA-based symbolic regression draws on the fundamental concepts of Darwinian evolution, such as breeding and variety, and applies modeling to assess the accuracy and relevance of the prediction models. In this technique, multiple models are generated among which the one with the optimal fit is chosen as the “parent” and the basis for “breeding” further models, called offspring, for the following generations. The present study finds a significant nonlinear relationship between lexicogrammatical knowledge and reading comprehension proficiency (R2 = .520). Details and computational requirements are discussed and implications for language assessment are explored.