Tabu Search
Tabu Search
From Hand Calculations to AI-Assisted Code
You can already ask an AI to write you a Tabu Search. The problem is knowing whether its memory is actually doing its job — or whether the tabu list is set so short it's cycling back on itself, or so long it's forbidden every useful move and painted itself into a corner. The entire method lives or dies on getting that memory right. This is the course that gives you that judgment.
Five parts, one algorithm, no gaps:
Part 1 — Intro. How a search that remembers where it's been — and deliberately forbids going back — can climb out of the local optima that trap simpler methods, and why one clever exception keeps that memory from becoming a straitjacket. The intuition before the notation.
Part 2 — By Hand. A complete worked example, move by move: evaluate the neighborhood, pick the best allowed move, update the tabu list, and apply the aspiration criterion when a forbidden move is too good to refuse — every candidate 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 search escape a local optimum — and see for yourself how the length of the tabu list changes where it ends up.
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 size the tabu list on purpose, set the aspiration criterion so good moves aren't needlessly blocked, 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.
