11 Best Python Genetic Algorithm Libraries

Here are some famous Python Genetic Algorithm Libraries. Some of the use cases of Python Genetic Algorithm Libraries include Designing and evolving computational models of biological systems, Automating the design of complex algorithms, developing a custom genetic algorithm, Generating Artificial Life, and Designing and growing digital systems. 

Python genetic library is a set of libraries and tools that can be used to analyze genetic data and build genetic algorithms in Python. It includes a variety of functions and classes for performing common genetic programming and analysis tasks, such as creating and manipulating genetic data, creating and manipulating genetic algorithms, and running simulations. 

Let us look at the libraries in detail below. 

biopython 

• Provides a convenient high-level interface to many commonly used bioinformatics tools. 
• Offers a wide range of sequence manipulation functions, such as translation. 
• Is a cross-platform library to use on different operating systems. 

DEAP 

• Uses an object-oriented design, making it easier to use and customize. 
• Includes a variety of genetic operators, selection algorithms, and other features. 
• Is built on top of NumPy, allowing for efficient vectorized operations on large datasets. 

Pyevolve 

• Offers many built-in selection, mutation, and crossover methods. 
• Has several built-in modules and functions for conveniently running experiments.  
• Is fast and efficient, allowing for short runtimes and quick results. 

EvolutionaryForest 

• Uses evolutionary algorithms to generate robust and diverse tree ensembles. 
• Implements the multi-objective optimization paradigm. 
• Highly parallelizable, allowing for efficient distributed computing.  

crianza  

• Supports both Python 2 and Python 3, making it compatible with a wider range of systems. 
• Allows for multicore processing, allowing faster and more efficient genetic programming.    
• Includes features such as fitness scaling, elitism, and tournament selection. 

gpFlappyBird 

• Highly modular and extensible, making it easy to create complex genetic algorithms. 
• Designed to be platform-independent, allowing it to be used in various environments.  
• Designed to be cross-platform, you can write code to work with Windows, Mac, and Linux.

PS-Tree 

• Has various features that make it suitable for many genetic programming tasks.   
• Has a simple and intuitive syntax, allowing users to set up and run experiments quickly.   
• Provides a suite of powerful tools for data exploration, data visualization, and model evaluation.  

zoonomia 

• Provides a broad range of genetic programming algorithms, from simple search algorithms to complex evolutionary algorithms.  
• Used to investigate and optimize complex problems.  
• Well-suited for applications that require many variables and complex relationships between them. 

pyGENP 

• Suited to both novice and experienced Python developers. 
• Uses an object-oriented approach, making it easy to use and extend.  
• Includes a number of visualization and analysis tools. 

gp2 

• Offers a wide range of features, including multiple data types. 
• Supports both single- and multi-objective optimization, as well as parallelization. 
• Very extensible, allowing users to create custom code blocks and write their own functions. 

GPLpy 

• Designed to provide a straightforward and intuitive interface for users. 
• Built on a modular architecture, it is easier to customize and extend.  
• Includes a unique debugger, offering users the ability to pause the program.