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/).
|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|
|Bloat control||No bloat control|