This project implements a Genetic Algorithm (GA) in C++ to solve a multidimensional data clustering optimization problem.
It was developed during university laboratory sessions, where the task included extending a provided baseline library with custom GA logic and performing live refactoring of the core algorithm.

• Generates the initial population
• Implements selection and crossover mechanisms
• Evaluates fitness values
• Runs the main evolutionary loop

• Stores the genotype representation
• Implements genetic operators:
  • Crossover
  • Mutation

A core requirement of the lab was a live refactoring of the crossover operator:

• Original implementation: Single-Point Crossover
• Refactored into: Modular Uniform Crossover
• Current state:
  • The refactored Uniform Crossover is the active implementation
  • The original Single-Point Crossover remains in the code as commented-out reference

This approach demonstrates refactoring skills and preserves the previous logic for comparison and further development.

The following files were supplied as the foundation of the project, defining:

• the problem space,
• the evaluation metric,
• data point representation.

• GroupingEvaluator.cpp / .h
• GaussianGroupingEvaluatorFactory.cpp / .h
• Point.cpp / .h