genetic-algorithm

To solve combinatorial optimization problems, encode solutions as chromosomes and apply modular genetic algorithm components including population handling, fitness evaluation, selection, crossover, mutation, and elitism using Python.

In multi-objective optimization, a genetic algorithm explores large, non-differentiable, multi-modal search spaces by evolving solutions through fitness evaluation and tournament selection, extending easily to NSGA-II style approaches.

Yes, you need Python with numpy and matplotlib installed to run the included usage examples that minimize sample objectives like the Rastrigin function and visualize the genetic algorithm optimization process.

Yes, you can use this genetic algorithm framework for feature selection by encoding feature subsets as chromosomes and applying the built-in fitness evaluation, crossover, and mutation operations to find optimal combinations.

You should choose a genetic algorithm over other optimization methods when your problem involves a large, non-differentiable, or multi-modal search space where traditional gradient-based methods fail to find optimal discrete solutions.