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:

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