Abstract
To mechanistically understand the dynamics of complex ecosystems, Yize Chen et al. employ symbolic regression (SR), a machine learning method that automatically reverse-engineers both model structure and parameters from temporal data. SR randomly assembles candidate models, computes the model fitness, and employs mutation and crossover to build better ones. The Pareto front reflects the trade-off between complexity and fitness of candidate models. More details can be found in article number 1900069 by Yize Chen et al.
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