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
The MATH Dataset

Measuring Mathematical Problem Solving With the MATH Dataset This is the repository for Measuring Mathematical Problem Solving With the MATH Dataset b

Dan Hendrycks 267 Dec 26, 2022
The second project in Python course on FCC

Assignment Write a function named add_time that takes in two required parameters and one optional parameter: a start time in the 12-hour clock format

Denise T 1 Dec 13, 2021
This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset.

DeepLab-ResNet-TensorFlow This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset. Up

19 Jan 16, 2022
Exe-to-xlsm - Simple script to create VBscript of exe and inject to xlsm

🎁 Exe To Office Executable file injection to Office documents: .xlsm, .docm, .p

3 Jan 25, 2022
Ludwig Benchmarking Toolkit

Ludwig Benchmarking Toolkit The Ludwig Benchmarking Toolkit is a personalized benchmarking toolkit for running end-to-end benchmark studies across an

HazyResearch 17 Nov 18, 2022
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

Mammoth - An Extendible (General) Continual Learning Framework for Pytorch NEWS STAY TUNED: We are working on an update of this repository to include

AImageLab 277 Dec 28, 2022
the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

EmbedSeg Introduction This repository hosts the version of the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

JugLab 88 Dec 25, 2022
Implementation of Convolutional enhanced image Transformer

CeiT : Convolutional enhanced image Transformer This is an unofficial PyTorch implementation of Incorporating Convolution Designs into Visual Transfor

Rishikesh (ऋषिकेश) 82 Dec 13, 2022
My personal code and solution to the Synacor Challenge from 2012 OSCON.

Synacor OSCON Challenge Solution (2012) This repository contains my code and solution to solve the Synacor OSCON 2012 Challenge. If you are interested

2 Mar 20, 2022
Rlmm blender toolkit - A set of tools to streamline level generation in UDK straight from Blender

rlmm_blender_toolkit A set of tools to streamline level generation in UDK straig

Rocket League Mapmaking 0 Jan 15, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 72 Dec 14, 2022
This repo contains research materials released by members of the Google Brain team in Tokyo.

Brain Tokyo Workshop 🧠 🗼 This repo contains research materials released by members of the Google Brain team in Tokyo. Past Projects Weight Agnostic

Google 1.2k Jan 02, 2023
Deep Dual Consecutive Network for Human Pose Estimation (CVPR2021)

Beanie - is an asynchronous ODM for MongoDB, based on Motor and Pydantic. It uses an abstraction over Pydantic models and Motor collections to work wi

295 Dec 29, 2022
[NeurIPS'20] Multiscale Deep Equilibrium Models

Multiscale Deep Equilibrium Models 💥 💥 💥 💥 This repo is deprecated and we will soon stop actively maintaining it, as a more up-to-date (and simple

CMU Locus Lab 221 Dec 26, 2022
[ICME 2021 Oral] CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning This repository is the official PyTorch implementation of CORE-Text, a

Jingyang Lin 18 Aug 11, 2022
Housing Price Prediction

This project aim was to predict the price of houses in the Boston area during the great financial crisis through regression, as well as classify houses into different quality categories according to

Florian Klement 1 Jan 27, 2022
A Re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing"

What is This This is a simple re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing"(1). Only Sections

102 Dec 14, 2022
Code repository for the paper "Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation" with instructions to reproduce the results.

Doubly Trained Neural Machine Translation System for Adversarial Attack and Data Augmentation Languages Experimented: Data Overview: Source Target Tra

Steven Tan 1 Aug 18, 2022
Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation

Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation Experiment Setting: CIFAR10 (downloaded and saved in ./DATA

John Seon Keun Yi 38 Dec 27, 2022