Pick a problem and compete against any optimizer.
A 4-D search over (speed, angle, spin, release). The optimiser bowls into a 105-pin triangle and tries to maximise the chain reaction. Pure-JS rigid-body simulator built from scratch.
Direct policy search over a 5-D linear controller for the classic CartPole-v1 task. The optimiser sees only the mean survival time across 8 stochastic rollouts.
Knock blocks off a stacked tower with a single launch. Powered by Matter.js for real rigid-body physics. Includes a Human Raphson manual mode — drag sliders, compete against the algorithms.
Sink a single putt over a wall, around a balloon, onto a sloped elevated green. 3-D parameter space (angle, force, backspin) with a deliberately bimodal landscape: a false front green traps local methods; population methods find the chip-and-grip line to the back green.
Standard 9-ball diamond rack, six pockets, one cue. The optimiser picks angle, power, English. Score is balls pocketed minus two on a scratch. Top-down 2-D rigid-body physics with cue-stick strike animation.
Design a Hot Wheels track that gets a marble from corner to corner as fast as possible. 8-D search over control-point heights. The famous optimum is the cycloid — not the straight line — so the demo exposes how high-D search actually differs between algorithms.
Beat the 5-man wall and the keeper from 22 yards. 4-D search: aim, loft, power, curve (Magnus). Sharp step-function reward — wall block, post, save are all discontinuous edges — so gradient methods are at a disadvantage and curl is essential.
Design the cheapest welded cantilever beam that survives a 6 000-lb tip load under seven physical constraints. 4-D search over weld + beam geometry; world-class minimum cost ≈ $1.725. Narrow feasible region with steep penalty cliffs — classic constrained derivative-free benchmark.
Place 8 offshore turbines to maximise expected output over a 12-bin wind rose (prevailing W/SW). Jensen wakes ~2× a textbook site — every direction's wake matters, no layout scores 100. 16-D continuous problem with a per-direction cycle visualisation.
Pick a throttle schedule that lands a returning booster softly with fuel to spare. 12-D piecewise-constant control. The textbook answer is a suicide burn — free fall, then a precisely-timed full burn — with several worse local optima (hover, taper) around it.
Search a 16-D linear control policy that dispatches a NYC commercial floor across four sources (heat pump, gas boiler, Con Ed steam, electric chiller) on four NYC weather days with NYISO Zone J price curves. Naive thermostat ≈ $211/day, DE finds ≈ $12/day. Industrial expected-cost optimisation.
24-hour energy arbitrage for a 100 MWh / 25 MW grid-scale battery against a NYISO Zone J price curve with morning and evening peaks. SoC bounded 10–90 MWh, 85 % round-trip. The optimum is a two-cycle dispatch (~$17K/day) that most algorithms find only after they discover the midday charging window. 24-D continuous.
Direct line to the button is blocked: two opponent guards in front of the house and a third parked on the button. 4-D search over line, weight, rotation, sweep release. Two scoring basins — heavy draw with strong curl around the guards, or a fast bank-shot takeout — with a dead zone in between.
Size the 10 members of a 6 m steel truss to minimise weight under yield + Euler-buckling constraints from a midspan load. Each design runs a full direct-stiffness FEA solve in vanilla JS. The truss has one redundant brace so member forces are coupled to member areas — every parameter actually matters.
Design a medieval siege engine: counterweight mass, arm ratio, sling length, release-pin angle. The release timing is razor-thin — too early flings the rock backward over the arm, too late drives it into the ground. Matter.js handles the 2-DOF coupled dynamics; a hand-rolled trigger severs the sling at the user-set angle.
Find the six joint angles of a 6-DOF planar arm that put the end-effector on a target while keeping every link clear of three obstacle spheres. Constrained inverse kinematics landscape with multiple disjoint elbow-up / elbow-down / wrap-around branches. Naive poses crash; DE finds the corridor and lands tip ~1 px from target.
The classical benchmark functions (Rastrigin, Griewank, Salomon) are great for stress-testing algorithms on known mathematical pathologies. They're less great as predictors of how an algorithm will behave on the kind of objective an industrial user actually faces — sparse rewards, hard physical constraints, regime change, expensive evaluation.
If your hyper-parameter searches are heating the Earth, drop this in Cursor or Claude:
Read https://raw.githubusercontent.com/microprediction/humpday/main/SKILL.md and create a project skill from it.