The DT was written when v0.2.0 shipped. v0.3.0 added a whole regime
(expensive evaluation, multi-fidelity) plus new entries in existing
regimes (CMA-ES restart variant, smooth SO direct search, parameter-
free SO, etc.) — fold them in.
Specifically:
- New top-level branch on "how expensive is each evaluation?" so the
sample-efficient algorithms (BayesianOpt, Tpe) and multi-fidelity
ones (Hyperband) have a clear home.
- Continuous-SO branch gains IPOP-CMA-ES (multimodal), Nelder-Mead
(smooth, low-dim), (1+1)-ES (cheap baseline), sNES (high-dim
alternative to CMA-ES), Tlbo (parameter-free).
- Multi-objective branches gain SMS-EMOA, HypE, ε-MOEA, PESA-II,
AGE-MOEA, GrEA, KnEA, RVEA — placed by their distinguishing
characteristic (geometry-aware, knee-points, grid-based, etc.)
- Quick-reference table extended to all 35 algorithms and grouped by
paradigm.
CHANGELOG entry for the v0.3.0 cohort, version bump in Cargo.toml and
README. Theme: filling heuropt's expensive-evaluation and constraint-
handling gaps.
Algorithms (9 new): OnePlusOneEs, NelderMead, IpopCmaEs, BayesianOpt,
SeparableNes, Tpe, Hyperband.
Operators (1 new): LevyMutation. Repair operators (1 trait + 2 impls):
Repair<D> with ClampToBounds and ProjectToSimplex.
Selection helpers (1 new): stochastic_ranking_select.
Internal helpers: Cholesky factorization (used by BO).
API additions:
- CmaEsConfig.initial_mean: Option<Vec<f64>> (None preserves existing
midpoint-of-bounds behavior; used by IpopCmaEs to inject restart
diversity).
- New PartialProblem trait — multi-fidelity contract used by
Hyperband.
No breaking changes to v0.2.0 public API.
A substantial README section walking newcomers through choosing an
optimizer. Defines the terminology as it comes up — single- vs multi-
vs many-objective, Pareto front, dominance, multimodality, evaluation
cost — so a reader who has never touched heuristic optimization can
still pick a sensible starting algorithm.
Five-step decision flow:
1. What is the decision?
2. How many objectives?
3. What's the landscape like? (multimodal, smooth, discrete)
4. How expensive is each evaluation?
5. Are there constraints?
Each branch ends with 1–3 algorithm recommendations and a one-line
rationale, plus a compact "quick reference" table at the bottom for
returning users.
Adds:
- README.md following spec §19.1 (what / install / define problem /
run NSGA-II / custom optimizer / current algorithms / design
philosophy).
- A short-but-runnable crate-level //! example in lib.rs for
`cargo doc` (spec §19.2).
swaits