Artificial chemistries—a review
Artificial Life
Biomimetic Representation with Genetic Programming Enzyme
Genetic Programming and Evolvable Machines
Bacterially inspired evolving system with an application to time series prediction
Applied Soft Computing
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Biochemical networks display a wide range of behaviors. While many of these networks tend to operate in a steady-state regime, others exhibit distinctly stochastic behaviors. Fitting models to data from these systems challenges many of the linear and steady-state assumptions of typical modeling techniques. The genetic algorithm described herein seeks to generate networks which exhibit desired average/steady-state behaviors while minimizing or maximizing the standard deviation of those behaviors.