Genetic Algorithm

Genetic Algorithm

From Hand Calculations to AI-Assisted Code

You can already ask an AI to write you a Genetic Algorithm. The problem is knowing whether it's genuinely evolving toward a better answer — or whether the population went uniform in the first dozen generations and every run since has just been reshuffling the same solution. This is the course that gives you that judgment.

Five parts, one algorithm, no gaps:

• Part 1 — Intro. How selection, crossover, and mutation — the same three moves that drive biological evolution — search a space of candidate solutions without ever needing a derivative or a smooth landscape. The intuition before the notation.

• Part 2 — By Hand. A complete worked example, generation by generation: encode the chromosomes, evaluate fitness, select, cross over, mutate — every bit and every number shown. Nothing waved away, nothing left "as an exercise." You prove the algorithm to yourself.

• Part 3 — In Excel. The same example built out in a spreadsheet you can open and poke at, so you watch the population converge — and see for yourself what changing the mutation rate or selection method actually does to the search.

• Part 4 — In Python. A clean, executable, verified reference implementation — the known-good version you can check any other code against.

• Part 5 — With AI. A tested prompt that directs an AI assistant to carry this same algorithm to your problem, plus the understanding to catch it when it's wrong.

By the end you won't just have working code. You'll have followed the algorithm from first principles through a hand calculation, a spreadsheet, and production Python — so when you point an AI at your own optimization problem, you'll know how to encode it, tune selection pressure and mutation on purpose, and tell when the answer is right.

Written for practicing engineers and STEM professionals who'd rather understand a method than trust a black box.