Premium
BioEssays 12/2019
Author(s) -
Chen Yize,
Angulo Marco Tulio,
Liu YangYu
Publication year - 2019
Publication title -
bioessays
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.175
H-Index - 184
eISSN - 1521-1878
pISSN - 0265-9247
DOI - 10.1002/bies.201970121
Subject(s) - crossover , symbolic regression , chen , computer science , mutation , regression , artificial intelligence , symbolic dynamics , machine learning , biology , statistics , genetic programming , mathematics , ecology , genetics , gene , pure mathematics
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.