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Beginner-friendly Jupyter-ready Python implementation and explanation of a Genetic Algorithm to optimize the Rastrigin function using roulette-wheel selection a
This skill provides a step-by-step, beginner-focused implementation of a Genetic Algorithm (GA) in pure Python to optimize the Rastrigin benchmark function. It is written and structured for Jupyter notebooks with four sections: configuration, reusable functions (fitness, selection, crossover, mutation), the evolution loop, and results output. The material explains algorithm choices (roulette wheel selection, one-point crossover, Gaussian mutation) and enforces constraints like fixed population size and no elitism.
Use this skill when you need a clear educational GA example for teaching, experimentation, or quick benchmarking on the Rastrigin function. It's ideal for instructors, students, or practitioners who want a minimal-dependency implementation (no numpy) that demonstrates core GA mechanics and prints final individuals for inspection.
This skill is best suited for code-capable agents and tooling that can run or author Python notebooks (Copilot/Codex-style agents, Claude Code, Cursor). It assumes the agent can produce runnable Python code and explanatory markdown.
This skill has not been reviewed by our automated audit pipeline yet.
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