# Getting Started
OptiGen: A Python Genetic Algorithm Library
OptiGen is a Python library that simplifies the implementation of genetic algorithms for solving optimization problems. It provides a set of classes and functions to create, evolve, and evaluate populations of potential solutions.
## Installation
To use OptiGen, you can install it using pip:
`pip install optigen`
## Example Usage
Here's an example of how to use OptiGento evolve a population to match a predefined output pattern:
```
from OptiGen import next_generation, Phenotype
if __name__ == "__main__":
training_data = Training_Data()
phenotypes = []
output = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
thresh_hold = 0.99
population_size = 100
mutation_rate = 0.001
max_generations = 1000
for __ in range(population_size):
phenotype = Phenotype(len(output),[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
for x in range(len(phenotype.result)):
if phenotype.result[x] == output[x]:
phenotype.fitness += 1 / len(output)
phenotypes.append([phenotype.fitness, phenotype.result])
phenotypes.sort(reverse=True)
print(f"Generation 0: Best score: { phenotypes[0]}")
training_data.original_data.append(phenotypes[0][0])
for gen in range(max_generations):
next_phenotypes = next_generation(phenotypes, mutation_rate).new_generation
phenotypes = []
for x in next_phenotypes:
phenotype = Phenotype(len(output), x.result)
for y in range(len(phenotype.result)):
if phenotype.result[y] == output[y]:
phenotype.fitness += 1 / len(output)
phenotypes.append([phenotype.fitness, phenotype.result])
phenotypes.sort(reverse=True)
print(f"Generation {gen + 1}: Best score: {phenotypes[0][0]} Result: {phenotypes[0][1]}")
training_data.original_data.append(phenotypes[0][0])
if phenotypes[0][0] >= thresh_hold:
break
training_data.show_graph()
```
Full Documentation: [GitHub](https://github.com/ShadowFlameFox/OptiGen/wiki/Documentation)
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"description": "# Getting Started\r\n\r\nOptiGen: A Python Genetic Algorithm Library\r\n\r\nOptiGen is a Python library that simplifies the implementation of genetic algorithms for solving optimization problems. It provides a set of classes and functions to create, evolve, and evaluate populations of potential solutions.\r\n\r\n## Installation\r\nTo use OptiGen, you can install it using pip:\r\n\r\n\r\n`pip install optigen`\r\n\r\n## Example Usage\r\nHere's an example of how to use OptiGento evolve a population to match a predefined output pattern:\r\n\r\n```\r\nfrom OptiGen import next_generation, Phenotype\r\n\r\nif __name__ == \"__main__\":\r\n\r\n training_data = Training_Data()\r\n\r\n phenotypes = []\r\n\r\n output = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n\r\n thresh_hold = 0.99\r\n population_size = 100\r\n mutation_rate = 0.001\r\n max_generations = 1000\r\n\r\n for __ in range(population_size):\r\n phenotype = Phenotype(len(output),[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\r\n for x in range(len(phenotype.result)):\r\n if phenotype.result[x] == output[x]:\r\n phenotype.fitness += 1 / len(output)\r\n phenotypes.append([phenotype.fitness, phenotype.result])\r\n phenotypes.sort(reverse=True)\r\n print(f\"Generation 0: Best score: { phenotypes[0]}\")\r\n training_data.original_data.append(phenotypes[0][0])\r\n\r\n for gen in range(max_generations):\r\n next_phenotypes = next_generation(phenotypes, mutation_rate).new_generation\r\n phenotypes = []\r\n for x in next_phenotypes:\r\n phenotype = Phenotype(len(output), x.result)\r\n for y in range(len(phenotype.result)):\r\n if phenotype.result[y] == output[y]:\r\n phenotype.fitness += 1 / len(output)\r\n phenotypes.append([phenotype.fitness, phenotype.result])\r\n phenotypes.sort(reverse=True)\r\n\r\n print(f\"Generation {gen + 1}: Best score: {phenotypes[0][0]} Result: {phenotypes[0][1]}\")\r\n training_data.original_data.append(phenotypes[0][0])\r\n if phenotypes[0][0] >= thresh_hold:\r\n break\r\n training_data.show_graph()\r\n```\r\nFull Documentation: [GitHub](https://github.com/ShadowFlameFox/OptiGen/wiki/Documentation)\r\n",
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