Cookbook

Short, focused recipes for the patterns that come up in practice. Each recipe is self-contained and small enough to copy into your own project.

Recipes

• Parallelize evaluation with rayon — when your evaluate is non-trivial CPU work, the parallel feature pays for itself almost immediately.
• Async evaluation — when your evaluate is IO-bound (HTTP / RPC / subprocess), the async feature lets the optimizer await many evaluations concurrently. The differentiating feature vs other optimization libraries.
• Tune a model with expensive evaluations — Bayesian Optimization, TPE, and Hyperband for the 50–500-eval regime.
• Compare two algorithms on your problem — multi-seed harness pattern straight from examples/compare.rs.
• Optimize a permutation (TSP-style) — the permutation operator toolkit (OX / PMX / CX / ERX + Inversion / Insertion / Scramble), plus Ant Colony for distance-matrix TSP.
• Multi-objective combinatorial problems — bi-objective TSP, bi-objective knapsack (Vec<bool>), and 3-objective JSS via NSGA-II / NSGA-III.
• Constrain your search with Repair — bounds, simplex projection, custom repair.
• Pick one answer off a Pareto front — the a-posteriori weighted-decision pattern from the jiggly_tuning example.
• Explore your results in a webapp — export an OptimizationResult to JSON and browse it interactively at heuropt-explorer — parallel coordinates, scatter, range filters, weighted ranking.
• Write your own algorithm — implement Optimizer<P> from scratch, à la the examples/custom_optimizer.rs walkthrough.