@stsoftware/neat-ai@0.121.0

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# NEAT Neural Network for DenoJS

<p align="left">
  <img width="100" height="100" src="docs/logo.png" align="right">
This project is a unique implementation of a neural network based on the NEAT (NeuroEvolution of Augmenting Topologies) algorithm, written in DenoJS using TypeScript.
</p>

## Feature Highlights

1. **Extendable Observations**: The observations can be extended over time as
   the indexing is done via UUIDs, not numbers. This prevents the need to
   restart the evolution process as new observations are added, providing
   flexibility and scalability.

2. **Distributed Training**: Training and evolution can be run on multiple
   independent nodes. The best-of-breed creatures can later be combined on a
   centralized controller node. This feature allows for distributed computing
   and potentially faster training times, enhancing the efficiency of the
   learning process.

3. **Life Long Learning**: Unlike many pre-trained neural networks, this project
   is designed for continuous learning, making it adaptable and potentially more
   effective in changing environments. This feature ensures the model remains
   relevant and accurate over time.

4. **Efficient Model Utilization**: Once trained, the current best model can be
   utilized efficiently by calling the `activate` function. This allows for
   quick and easy deployment of the trained model.

5. **Unique Squash Functions**: The neural network supports unique squash
   functions such as IF, MEAN, MAX, MIN, and HYPOT. These functions provide more
   options for the activation function, which can lead to different network
   behaviours, offering a wider range of potential solutions. More about
   [Activation Functions](https://en.wikipedia.org/wiki/Activation_function).

6. **Neuron Pruning**: Neurons whose activations don't vary during training are
   removed, and the biases in the associated neurons are adjusted. This feature
   optimizes the network by reducing redundancy and computational load. More
   about
   [Pruning (Neural Networks)](https://en.wikipedia.org/wiki/Pruning_(neural_networks)).

7. **CRISPR**: Allows injection of genes into a population of creatures during
   evolution. This feature can introduce new traits and potentially improve the
   performance of the population. More about
   [CRISPR](https://en.wikipedia.org/wiki/CRISPR).

8. **Grafting**: If the child is a clone of one of the parents, the algorithm
   will graft neurons from the other parent onto the child. This helps maintain
   diversity in the population and prevents clones, ensuring more robust and
   varied evolutionary outcomes.

9. **Memetic Evolution**: The algorithm can now record and utilize the biases
   and weights of the fittest creatures to fine-tune future generations. This
   process, inspired by the concept of memes, allows the system to "remember"
   and build upon successful traits, enhancing the evolutionary process. Learn
   more about
   [Memetic Algorithms](https://en.wikipedia.org/wiki/Memetic_algorithm).

10. **[Visualization](https://stsoftwareau.github.io/NEAT-AI/index.html)**

## Usage

This project is designed to be used in a DenoJS environment. Please refer to the
[DenoJS documentation](https://deno.land/manual) for setup and usage
instructions.

## Contributions

Contributions are welcome. Please submit a pull request or open an issue to
discuss potential changes/additions.

## License

This project is licensed under the terms of the Apache License 2.0. For the full
license text, please see [LICENSE](./LICENSE).

[![Built with the Deno Standard Library](https://raw.githubusercontent.com/denoland/deno_std/main/badge.svg)](https://deno.land/std)