> For the complete documentation index, see [llms.txt](https://finch-1.gitbook.io/Finch3/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://finch-1.gitbook.io/Finch3/about.md).

# About

## Finch 3

### About:

Finch 3 is a genetic algorithm framework. It aims to be:

* understandable
* fast
* and capable

This is a very early version but here are some things you can expect:

* The ability to run genetic algorithms on a GPU
* Much better documentation than Finch2 (although this is a work in progress)
* Overall much faster, more consistent, and far less bugs.

#### Colab Notebooks

1: [Simple Finch Colab Demo](https://colab.research.google.com/drive/1QK7zYTBSkud4V6QQQRCi0ofkJ9bFp9B9?usp=sharing)

* Very simple genetic algorithm

2: [Trick Clip](https://colab.research.google.com/drive/1q_MDZAqofAbj-wkZHoWoWSP_6osymLGK?usp=sharing)

* A genetic algorithm built to create 'adversarial images' against the Clip image recognition model.

Many Google Colab notebooks will be added here soon!

### Installation

* **From Pip:**

`pip install finch-genetics`

* **From Git:**

run `git clone https://github.com/dadukhankevin/Finch`

#### Example

This example demonstrates the usage of Finch3, a genetic programming library, to evolve individuals in a sequential environment.

1. Import Finch:

   ```python
   from Finch.environments import Sequential
   from Finch.genepools import FloatPool
   from Finch.layers import *
   ```
2. Define a fitness function `fit` that evaluates the performance of an individual. This is a very simple fitness function, and will essentially help us evolve a list floats to slowly become higher and higher numbers. The fitness function *could* also simply turn the floats into weights in a neural network and then evaluate their performance, this concept is called neuroevolution.

   ```python
   def fit(individual):
       return sum(individual.genes)  # You can modify the fitness function to make it interesting
   ```
3. Configure the genetic algorithm parameters in the Config section.

   ```python
   # Config section
   length = 100
   pool_minimum = 0
   pool_maximum = 10
   population_size = 100
   amount_to_mutate = 10
   gene_selection = 5
   parent_count = 20
   children_count = 2
   max_population = 99
   evolution_steps = 1000
   min_mutation = -2
   max_mutation = 2
   ```
4. Create a FloatPool to define the gene pool for individuals.

   ```python
   # Creating the FloatPool
   pool = FloatPool(length=length, minimum=pool_minimum, maximum=pool_maximum)
   ```
5. Create a Sequential environment with various layers representing different genetic operations.

   ```python
   # Creating the Sequential environment
   environment = Sequential(layers=[
       Populate(pool, population=population_size),
       FloatMutateRange(individual_selection=amount_to_mutate, gene_selection=gene_selection,
                        min_mutation=min_mutation, max_mutation=max_mutation, keep_within_genepool_bounds=True),
       ParentSimple(parent_count, children=children_count),
       SortByFitness(),
       CapPopulation(max_population=max_population),
   ])
   ```
6. Run the genetic algorithm.

   ```python
   if __name__ == "__main__":
       # Compiling the environment with the fitness function
       environment.compile(fitness_function=fit)
       environment.evolve(evolution_steps)

       # Printing the best individual
       print("Here is the best individual:\n", environment.best_ever.genes)

       # Plotting the environment
       environment.plot()
   ```

Another example can be found in the /examples directory!


---

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