How to install and set up fungp in Clojure?

First install Leiningen, then add fungp as a dependency in your project.clj with [fungp '0.3.2'], and run 'lein deps' to download it. The README provides detailed Unix-like setup instructions, including using the REPL for experimentation.

What kind of fitness functions work best with fungp?

Fitness functions should return an error number where lower is better, such as mean squared error for symbolic regression. You can customize them based on your problem, as shown in the samples and tutorial code.

How does fungp compare to DEAP or other GP libraries?

fungp is Clojure-specific and focuses on functional programming integration, while libraries like DEAP are in Python with broader features. fungp is ideal for Clojure environments but has a smaller community and less tooling.

Can fungp handle classification or optimization problems?

Yes, by defining appropriate fitness functions and terminal/function sets, fungp can evolve programs for various tasks beyond symbolic regression, such as classification or algorithmic discovery, though it requires custom configuration.

How to configure the island model parameters in fungp?

Use options like num-islands, migrations, and iterations in the run-genetic-programming function to control population diversity and migration frequency, as documented in the function's parameter list.

Is fungp suitable for real-world production use?

Not recommended for production due to its academic nature and version 0.3.2; it's better for research, experimentation, or education where stability and support are less critical.

Open-Awesome

Fungp

GPL-3.0Clojure

A genetic programming library for Clojure that evolves programs using mutation, reproduction, and fitness functions.

Visit Website GitHub

100 stars12 forks0 contributors

What is Fungp?

fungp is a genetic programming library for Clojure that automatically evolves computer programs using processes inspired by biological evolution, such as mutation and reproduction. It solves problems like symbolic regression by generating trees of Clojure code and evaluating them with fitness functions to produce correct outputs.

Target Audience

Clojure developers and researchers interested in evolutionary computation, genetic programming, or automated program synthesis for tasks like symbolic regression or algorithmic discovery.

Value Proposition

Developers choose fungp for its native Clojure implementation, functional programming design, and configurable evolutionary parameters, providing a flexible and accessible tool for experimenting with genetic programming in a REPL-driven environment.

Overview

A genetic programming library for Clojure

Use Cases

Best For

Solving symbolic regression problems by evolving mathematical functions
Experimenting with genetic programming algorithms in Clojure
Automatically generating programs through evolutionary processes
Researching evolutionary computation with configurable parameters
Learning genetic programming concepts using a functional language
Developing custom fitness functions to evolve specialized programs

Not Ideal For

Projects requiring integration with non-JVM languages or ecosystems
Teams needing production-ready, commercially supported machine learning libraries
Users wanting graphical interfaces or automated pipelines for genetic programming

Pros & Cons

Pros

Clojure Native Integration

Built specifically for Clojure, it leverages the REPL for interactive development and functional programming style, as emphasized in the setup and tutorial examples.

Configurable Evolutionary Parameters

Offers extensive tuning options like iterations, migrations, mutation probability, and ADFs, allowing precise control over the GP process for diverse problems.

Island Model Diversity

Implements multiple populations with migrations to maintain genetic diversity, potentially improving solution quality by avoiding local optima, as described in the features.

Educational and Accessible

Includes thorough documentation, a tutorial, and sample code in the source, making it practical for learning genetic programming concepts in a functional context.

Cons

Academic Project Limitations

As a student project at version 0.3.2, it may lack the stability, frequent updates, and community support of more mature libraries, which could lead to bugs or abandoned development.

Clojure Knowledge Barrier

Requires familiarity with Clojure and setup with Leiningen, as outlined in the installation steps, creating a steep learning curve for developers outside the Clojure ecosystem.

Interpretability and Complexity

Evolved programs can become verbose and hard to understand, as shown in the sample output, making debugging and practical application challenging for non-experts.

Frequently Asked Questions

Related Projects

PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Stars101,899

Forks28,473

Last commit10 hours ago

keras

Deep Learning for humans

Streamlit — A faster way to build and share data apps.

Stars45,326

Forks4,331

Last commit15 hours ago

gradio

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

Stars43,191

Forks3,557

Last commit13 hours ago

Community-curated · Updated weekly · 100% open source

Found a gem we're missing?

Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.

Submit a project Star on GitHub