🧬 Genetic Algorithm Engine

Genetic Algorithm Engine is a general-purpose evolutionary computation framework built in Scala. It provides a modular foundation for solving optimization problems using biologically inspired mechanics such as mutation, crossover, and natural selection.

Tested on polynomial and linear regression, this engine can be extended to a wide variety of problem domains—from data fitting to symbolic reasoning.

“Not all those who wander are lost. Some are evolving.”
— On watching a fitness score rise

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📦 Features

• Generic Genetic Algorithm Framework – Reusable and adaptable to multiple problem domains.
• Fitness-Driven Evolution – Population members are scored, selected, and mutated over generations.
• Polynomial & Linear Regression – Demonstrated success fitting noisy data.
• Modular Design – Plug in new fitness functions, gene decoders, and chromosome layouts.
• Extensible & Elegant – Clean Scala implementation ready for expansion.

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🧠 How It Works

1. Initialization
   A population of individuals (chromosomes composed of Gene objects) is randomly generated.

2. Evaluation
   Each individual is decoded into a potential solution and scored by a fitness function.

3. Selection & Mutation
   Top individuals are cloned and mutated to populate the next generation.

4. Iteration
   This process continues for 90,000 generations by default, evolving better solutions over time.

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🧪 Example: Linear Regression

The following code uses the genetic algorithm to evolve a line that best fits a given set of 2D points:

val input: List[Point] = List(Point(1,1), Point(2,2))
val answer = linearRegression(input)
println(answer.slope + " " + answer.yIntercept)

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⚙️ Core API

geneticAlgorithm

geneticAlgorithm[T](
  fitnessFunction: T => Double,
  chromosomeDecoder: List[Gene] => T,
  sampleChromosome: List[Gene]
): T

• Evolves a solution of type T using genetic techniques
• Accepts user-defined fitness and decoding logic

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🧬 Example Components

Fitness Function

def linearReg1stParam(scatterPlot: Array[Point]): Line => Double

Computes fitness based on vertical distance from points to a candidate line.

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Gene Decoder

def linearReg2ndParam(): List[Gene] => Line

Converts genes into a Line object using scaled tangent functions for range diversity.

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Chromosome Template

def linearReg3rdParam(): List[Gene]

Provides an example chromosome layout for the regression use case.

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📂 Project Structure

genetics/
├── genes/             # Gene and chromosome utilities
├── geometry/          # Points and Lines in 2D space
└── GeneticAlgorithm.scala  # Main engine and regression logic

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🚀 Getting Started

Make sure you have Scala installed (2.13 or newer):

scalac GeneticAlgorithm.scala
scala genetics.GeneticAlgorithm

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🔮 Future Enhancements

• Multi-objective optimization
• Custom crossover strategies
• Visual evolution inspector
• Symbolic expression generation

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📄 License

MIT License. Use freely, fork boldly, and evolve responsibly.