A strongly-typed genetic programming framework for Python

Overview

monkeys

PyPI version

"If an army of monkeys were strumming on typewriters they might write all the books in the British Museum."

monkeys is a framework designed to make genetic programming in Python accessible, quick, flexible, and fun.

Get started here.

What is genetic programming?

Genetic programming algorithms are a class of evolutionary algorithms in which solutions to a problem are represented as executable tree structures - programs. In order to use genetic programming in order to solve a problem, two things must be specified:

  1. What form(s) a potential solution can take.

  2. A method of scoring solutions based on their meritoriousness.

Given these, a genetic programming system can perform intelligent exploration and search through the space of potential solutions, narrowing in on those programs that best solve the problem as specified. Genetic programming has achieved human-competitive results in a wide swath of domains, including:

monkeys to the rescue!

"Ford, there's an infinite number of monkeys outside who want to talk to us about this script for Hamlet they've worked out."

monkeys makes getting started with genetic programming painless and fun. Install monkeys by running:

pip install monkeys

monkeys uses a variant of genetic programming called "strongly-typed genetic programming" in order to allow you to quickly and easily specify how your programs should be structured.

monkeys supports Python 2.7 and 3.x.

Examples

Tutorials:

Sample usages:

Contacts

Owner
H. Chase Stevens
Metaprogramming, natural language processing, and global optimization technique enthusiast.
H. Chase Stevens
TF Image Segmentation: Image Segmentation framework

TF Image Segmentation: Image Segmentation framework The aim of the TF Image Segmentation framework is to provide/provide a simplified way for: Convert

Daniil Pakhomov 546 Dec 17, 2022
IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

IDRLnet IDRLnet is a machine learning library on top of PyTorch. Use IDRLnet if you need a machine learning library that solves both forward and inver

IDRL 105 Dec 17, 2022
A deep learning library that makes face recognition efficient and effective

Distributed Arcface Training in Pytorch This is a deep learning library that makes face recognition efficient, and effective, which can train tens of

Sajjad Aemmi 10 Nov 23, 2021
A Machine Teaching Framework for Scalable Recognition

MEMORABLE This repository contains the source code accompanying our ICCV 2021 paper. A Machine Teaching Framework for Scalable Recognition Pei Wang, N

2 Dec 08, 2021
Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning accelerators for distributed training using the Ray distributed

166 Dec 27, 2022
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation

PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation The paper: https://arxiv.org/abs/1704.03296 What makes

Jacob Gildenblat 322 Dec 17, 2022
Picasso: A CUDA-based Library for Deep Learning over 3D Meshes

The Picasso Library is intended for complex real-world applications with large-scale surfaces, while it also performs impressively on the small-scale applications over synthetic shape manifolds. We h

97 Dec 01, 2022
[CVPR'21] Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

IVOS-W Paper Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild Zhaoyun Yin, Jia Zheng, Weixin Luo, Shenhan Qian, Hanli

SVIP Lab 38 Dec 12, 2022
Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)

Poplar Implementation of Bundle Adjustment using Gaussian Belief Propagation on Graphcore's IPU Implementation of CVPR 2020 paper: Bundle Adjustment o

Joe Ortiz 34 Dec 05, 2022
Official code for "End-to-End Optimization of Scene Layout" -- including VAE, Diff Render, SPADE for colorization (CVPR 2020 Oral)

End-to-End Optimization of Scene Layout Code release for: End-to-End Optimization of Scene Layout CVPR 2020 (Oral) Project site, Bibtex For help conta

Andrew Luo 41 Dec 09, 2022
PyTorch implementation of DirectCLR from paper Understanding Dimensional Collapse in Contrastive Self-supervised Learning

DirectCLR DirectCLR is a simple contrastive learning model for visual representation learning. It does not require a trainable projector as SimCLR. It

Meta Research 49 Dec 21, 2022
Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'

Filtration Curves for Graph Representation This repository provides the code from the KDD'21 paper Filtration Curves for Graph Representation. Depende

Machine Learning and Computational Biology Lab 16 Oct 16, 2022
Prototypical Networks for Few shot Learning in PyTorch

Prototypical Networks for Few shot Learning in PyTorch Simple alternative Implementation of Prototypical Networks for Few Shot Learning (paper, code)

Orobix 835 Jan 08, 2023
Activity image-based video retrieval

Cross-modal-retrieval Our approach is focus on Activity Image-to-Video Retrieval (AIVR) task. The compared methods are state-of-the-art single modalit

BCMI 75 Oct 21, 2021
Official implementation of the paper "Lightweight Deep CNN for Natural Image Matting via Similarity Preserving Knowledge Distillation"

Lightweight-Deep-CNN-for-Natural-Image-Matting-via-Similarity-Preserving-Knowledge-Distillation Introduction Accepted at IEEE Signal Processing Letter

DongGeun-Yoon 19 Jun 07, 2022
Fast, differentiable sorting and ranking in PyTorch

Torchsort Fast, differentiable sorting and ranking in PyTorch. Pure PyTorch implementation of Fast Differentiable Sorting and Ranking (Blondel et al.)

Teddy Koker 655 Jan 04, 2023
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS 2020) Introduction AdaShare is a novel and differentiable approach fo

94 Dec 22, 2022
Companion repo of the UCC 2021 paper "Predictive Auto-scaling with OpenStack Monasca"

Predictive Auto-scaling with OpenStack Monasca Giacomo Lanciano*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella 2021 IEEE/ACM 14t

Giacomo Lanciano 0 Dec 07, 2022
Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

On the Bottleneck of Graph Neural Networks and its Practical Implications This is the official implementation of the paper: On the Bottleneck of Graph

75 Dec 22, 2022