Cartesian Genetic Programming in pure Python.
Project description
HAL-CGP
Cartesian genetic programming (CGP) in pure Python.
This library implements Cartesian genetic programming (e.g, Miller and Thomson, 2000; Miller, 2011) for symbolic regression in pure Python, targeting applications with expensive fitness evaluations. It provides Python data structures to represent and evolve two-dimensional directed graphs (genotype) that are translated into computational graphs (phenotype) implementing mathematical expressions. The computational graphs can be compiled as a Python functions, SymPy expressions (Meurer et al., 2017) or PyTorch modules (Paszke et al., 2017). The library currently implements an evolutionary algorithm, specifically (mu + lambda) evolution strategies adapted from Deb et al. (2002), to evolve a population of symbolic expressions in order to optimize an objective function.
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