Implement genetic algorithm for optimizing continuous functions

[UTSAVS26]

Describe your change:

• Added a flexible genetic algorithm that allows users to define their own target functions for optimization.

• Included features for population initialization, fitness evaluation, selection, crossover, and mutation.

• Example function provided for minimizing f(x, y) = x^2 + y^2.

• Configurable parameters for population size, mutation probability, and generations.

• Add an algorithm? ✅ Yes

• Fix a bug or typo in an existing algorithm? ❌ No

• Add or change doctests? ❌ No

• Documentation change? ❌ No

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.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues, then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

Fixes #11578

[UTSAVS26]

Hi @cclauss , Thank you for your guidance on PR #11670 i am able to pass all the checks successfully in this PR.
Thank you once again.

[UTSAVS26]

And how long it takes for the PR to get merged?
Can i raise new PR's till then or wait until this PR is merges?

[UTSAVS26]

@cclauss please review and merge my PR.

[UTSAVS26]

@cclauss please review my pr as i want to contribute more. thank you for your time.

[UTSAVS26]

@cclauss

[UTSAVS26]

@cclauss please review my code.