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All Projects → moshesipper → tiny_gp

moshesipper / tiny_gp

Licence: GPL-3.0 License
Tiny Genetic Programming in Python

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evolutiongenetic-algorithmgenetic-programmingevolutionary-algorithmsevolutionary-algorithmevolution-simulationevolutionary-computationevolutionarygenetic-programming-algorithmtiny-genetic-programming

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Tiny Genetic Programming in Python

A minimalistic program implementing Koza-style (tree-based) genetic programming to solve a symbolic regression problem.

tiny-gp.py is a basic (and fully functional) version, which produces textual output of the evolutionary progression and evolved trees.

tiny-gp-plus.py displays dynamic graphs of error and mean tree size (size = number of nodes), has a bloat-control option, and produces nicer, graphic output (you'll need to install https://pypi.org/project/graphviz/).

If you wish to cite this:

@misc{Sipper2019tinyGP,
  author = {Sipper, M.},
  title = {Tiny Genetic Programming in Python},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/moshesipper/tiny_gp} }
}
Symbolic Regression using GP
Objective Find an expression with one input (independent variable x), whose output equals the value of the quartic function x4 + x3 + x2 + x + 1
Function set add, sub, mul
Terminal set x, -2, -1, 0, 1, 2
Fitness Inverse mean absolute error over a dataset of 101 target values, normalized to [0,1]
Paremeters POP_SIZE (population size), MIN_DEPTH (minimal initial random tree depth), MAX_DEPTH (maximal initial random tree depth), GENERATIONS (maximal number of generations), TOURNAMENT_SIZE (size of tournament for tournament selection), XO_RATE (crossover rate), PROB_MUTATION (per-node mutation probability)
Termination Maximal number of generations reached or an individual with fitness = 1.0 found
Evolved solution Another evolved solution
GPTree GPTree2
Bloat control No bloat control
GP run GP run
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