Added an algorithm in how to use genetic algorithm for numbers

[harshajv7981]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
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[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

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[algorithms-keeper]

Please provide return type hint for the function: genetic_algorithm. If the function does not return a value, please provide the type hint as: def function() -> None:

[harshajv7981]

Updated

[priya-sundaram-dev]

Thanks @harshajv7981. The fitness_function part is fine (type hints + working doctests), but I think the core needs rethinking before this fits genetic_algorithm/:

1. It isn't a genetic algorithm. genetic_algorithm() draws 100 random numbers once and returns the best by fitness — that's a single round of random search, with no population evolution: no selection, crossover, mutation, or generations. A GA contribution should show at least those steps iterating over multiple generations (see genetic_algorithm/basic_string.py in the repo for the expected shape).
2. The doctests don't validate anything. Both assertions in genetic_algorithm() are written to evaluate to False and expect False:

   >>> abs(best_solution - (-1.45)) < 0.1
   False
   

   So the doctest "passes" without demonstrating a correct result. (For reference, x**2 - 3x + 2 is minimised at x = 1.5 on [-2, 2], giving -0.25, so -1.45/6.0 aren't the target anyway.) After reworking into a real GA, please seed RNG and assert a meaningful convergence property.

Happy to re-review once it evolves a population over generations — the fitness scaffolding is a good start.