StandardGP

StandardGP is a genetic programming regression algorithm based on original GP operators. To provide a comparison algorithm between new linear GP variants and StandardGP - this is an easy to use pure Python SGP algorithm. The goal of this project is to get a better understanding of GP algorithms through visualizations. How to learn about the search space, to transfer knowledge between domains and how to evolve optimal generalizing operators.

Features

• True vectorized fitness evaluation
• Blow control without fitness intervention via semi-stable Crossover
• Multithreaded optimization
• Subtree-Cache that includes the isomorphism between functions
• Subtree-Mutation based on fitness
• Subtree-Crossover determines a good internal node based on tree-size
• Function-space reduction via scale and location invariance

Install:

git clone https://github.com/thegenius89/StandardGP.git
cd StandardGP
python setup.py install

Usage:

import numpy as np
from standardgp import GP

x = np.random.rand(200, 5) * 4 - 2
y = np.sin(x[:, 0] * x[:, 0]) * np.cos(x[:, 1] - 1) * 188 - 243
gp = GP(x, y)
best_fit, model = gp.run(show=True, threads=8)
print("Fit: {}, Model: {}".format(best_fit, model))
print("RMSE:", np.sqrt(np.mean(np.square(gp.predict(x) - y))))

You can see how huge your dataset can be without losing performance by changing the 200 to say 2000.

Advanced usage:

All the hyperparameters are very good chosen - for many cases there is no need to adjust them. The [...,...] are recommended ranges.

{
    "gens"       : 100,     # [1, n] stop after n generations
    "pop_size"   : 4000,    # [1, n] number of trees in population
    "elites"     : 0.15,    # [0, 0.5] % elite copies per generation
    "crossovers" : 0.60,    # [0, 2.0] % inplace crossover on population
    "mutations"  : 0.10,    # [0, 1.0] % probabillity per tree per gen
    "max_nodes"  : 24,      # [8, n] max nodes per tree
    "cache_size" : 500000,  # max ndarrays in cache (look at your RAM)
    "precision"  : 1e-8,    # precision termination condition
}

Just pass a config to the GP

gp = GP(x, y, cfg={"mutations": 0.25, ...})

Supported Operators/Functions/Constants

+, -, *, /[p]
sin, cos, tan, exp[p], sqrt[abs], log[p], square[max]
pi, pi/2, 1, 2, 0.5, uniform(0, 10)

Coming

• Full vectorized GP-Operators that are easy to automatically adapt
• Meta learner with knowledge transfer between thousands of problems
• Meta learner with programmatically evolved operators
• Visualization of evolved operators
• Register repo for the "Living Benchmark Suite"

https://github.com/cavalab/srbench

• pip install standardgp

Visions

I think it's clear for the GP community that evolving systems based on formal logic are in principle an unimaginable amount more efficient then artificial neural networks in doing the same task. These networks are just a subclass of a system that can create arbitrary complex programs. GP will be able to build optimal neural network architectures. Unfortunately these systems are way more complex than ANNs and it will take time to catch up ANNs with GP.