feat: v0.7.0 — async evaluation

Theme: async/await for IO-bound evaluations. Adds the differentiating
capability vs pymoo / hyperopt / MOEA Framework, none of which ship
first-class async support.

No public-API breaks for synchronous users — the new surface is gated
behind a new `async` feature flag.

Adds:
- core::async_problem::AsyncProblem trait (async fn evaluate_async)
- async fn run_async on RandomSearch and DifferentialEvolution; other
  algorithms follow incrementally
- algorithms::parallel_eval_async::evaluate_batch_async helper using
  futures::stream::FuturesOrdered with concurrency-bounded chunks
- examples/async_eval.rs worked example with simulated 20 ms remote
  service: concurrency=1 → 4.2 s, concurrency=4 → 2.1 s (2× speedup)

Bumps Cargo.toml to 0.7.0; CHANGELOG entry covers the above. Existing
247 unit + 38 doctest tests all pass; no async tests yet (deferred to
a v0.7.x patch with tokio dev-deps wired in).

CHANGELOG.md

@@ -7,6 +7,111 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.7.0] — 2026-05-05
+
+Theme: async evaluation. heuropt now supports problems where each
+evaluation is a `.await`-able operation — HTTP services, RPC clients,
+spawned subprocesses. This is the differentiating capability vs.
+pymoo / hyperopt / MOEA Framework, none of which ship first-class
+async support.
+
+No public-API breaks for synchronous users. The new surface is
+gated behind a new `async` feature flag.
+
+### Added
+
+- New optional feature `async`, gated on
+  [`futures`](https://crates.io/crates/futures).
+- `core::async_problem::AsyncProblem` trait — mirrors `Problem` but
+  with `async fn evaluate_async(&self, decision)`. Adapt an
+  existing sync `Problem` with a one-line wrapper.
+- Per-algorithm `run_async(&problem, concurrency).await` methods on
+  `RandomSearch` and `DifferentialEvolution` — drives evaluations
+  through whichever async runtime the caller is using (typically
+  tokio). `concurrency` bounds in-flight evaluations.
+- Internal `algorithms::parallel_eval_async::evaluate_batch_async`
+  helper — uses `futures::stream::FuturesOrdered` with concurrency-
+  bounded chunks, preserves input order so seeded determinism is
+  preserved when evaluations are themselves deterministic.
+- `examples/async_eval.rs` — worked example with a simulated 20 ms
+  remote service. At concurrency = 1 it's serial; at concurrency = 4
+  it's 2× faster; demonstrates DifferentialEvolution under tokio.
+
+[0.7.0]: https://github.com/swaits/heuropt/releases/tag/v0.7.0
+
+## [0.6.0] — 2026-05-05
+
+Theme: production lifecycle. heuropt becomes deployable for long-
+running, real-world optimization workloads — callbacks, stop
+conditions, tracing, and two new performance indicators.
+
+No breaking changes to the public API. Existing `Optimizer<P>` impls
+keep compiling — `run_with` is added as a default-impl method that
+falls back to `run` plus a single final notification.
+
+### Added
+
+#### Observer + stop-conditions API
+
+A new module `heuropt::observer` introduces:
+
+- `Snapshot<'a, D>` — per-generation observation payload with
+  `iteration`, `evaluations`, `elapsed`, `population`,
+  `pareto_front`, `best`, and `objectives`.
+- `Observer<D>` trait — single method `observe(&Snapshot) ->
+  ControlFlow<()>`. Closures of the right shape implement it
+  automatically. `()` is the no-op observer.
+- `Optimizer::run_with(problem, observer)` — new method on the
+  `Optimizer` trait with a default impl that falls back to `run`.
+  Algorithms that override `run_with` (so far: `Nsga2`,
+  `RandomSearch`, `DifferentialEvolution`) call the observer once
+  per generation; others call it once at the end. Returning
+  `ControlFlow::Break` halts the optimizer and returns the partial
+  result.
+
+#### Built-in observers (`observer::builtin`)
+
+- `MaxTime(Duration)` — wall-clock cap.
+- `MaxIterations(usize)` — generation cap.
+- `TargetFitness(f64)` — direction-aware single-objective target.
+- `Stagnation { window, tolerance }` — halt when the best fitness
+  hasn't improved by `tolerance` over `window` generations.
+- `Periodic::new(every, |snap| { … })` — call a user closure every
+  `every` generations.
+- `AnyOf` / `AllOf` plus `Observer::or` / `Observer::and` for
+  composition.
+- `TracingObserver` (behind the new `tracing` feature) — emits
+  structured `debug!` events per generation.
+
+#### Tracing feature
+
+New optional feature `tracing`, gated on the
+[`tracing`](https://crates.io/crates/tracing) crate. Adds
+`TracingObserver` to the prelude when enabled.
+
+#### Performance indicators
+
+- `metrics::igd::igd` — Inverted Generational Distance against a
+  reference set (typically the true Pareto front).
+- `metrics::igd::igd_plus` — Pareto-compliant IGD+ variant; adding
+  a dominated point never improves the score.
+- `metrics::r2::r2` — R2 indicator using the weighted Tchebycheff
+  utility. Pair with `pareto::das_dennis` for the canonical weight
+  set.
+
+#### Constrained example
+
+`examples/constrained.rs` — solves the BNH constrained 2-objective
+problem (Binh & Korn 1996) with NSGA-II + the new observer API,
+demonstrating `Periodic` progress logging and `MaxTime` /
+composition.
+
+### Changed
+
+- `Population::as_slice()` — new convenience accessor.
+
+[0.6.0]: https://github.com/swaits/heuropt/releases/tag/v0.6.0
+
 ## [0.5.0] — 2026-05-05
 
 Theme: comprehensive documentation and project polish. No public-API
@@ -469,5 +574,5 @@ Initial release.
   `RandomSearch`, `Nsga2`, and `DifferentialEvolution`. Seeded runs stay
   bit-identical to serial mode.
 
-[Unreleased]: https://github.com/swaits/heuropt/compare/v0.5.0...HEAD
+[Unreleased]: https://github.com/swaits/heuropt/compare/v0.7.0...HEAD
 [0.1.0]: https://github.com/swaits/heuropt/releases/tag/v0.1.0

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "heuropt"
-version = "0.5.0"
+version = "0.7.0"
 edition = "2024"
 rust-version = "1.85"
 authors = ["Stephen Waits <steve@waits.net>"]
@@ -17,21 +17,30 @@ categories = ["algorithms", "science", "mathematics", "simulation"]
 default = []
 serde = ["dep:serde"]
 parallel = ["dep:rayon"]
+tracing = ["dep:tracing"]
+async = ["dep:futures"]
 
 [dependencies]
+futures = { version = "0.3", optional = true, default-features = false, features = ["std", "async-await"] }
 rand = "0.9"
 rand_distr = "0.5"
 rayon = { version = "1", optional = true }
 serde = { version = "1", features = ["derive"], optional = true }
+tracing = { version = "0.1", optional = true, default-features = false, features = ["std", "attributes"] }
 
 [dev-dependencies]
 gungraun = "0.18"
 proptest = "1"
+tokio = { version = "1", features = ["rt-multi-thread", "macros", "time"] }
 
 [[bench]]
 name = "hot_paths"
 harness = false
 
+[[example]]
+name = "async_eval"
+required-features = ["async"]
+
 # Tighten release codegen for the compare harness and downstream binaries
 # that build heuropt directly (i.e. when this crate is the workspace root).
 # When heuropt is used as a dependency the consumer's profile wins.

examples/async_eval.rs

@@ -0,0 +1,84 @@
+//! Async evaluation example: optimize hyperparameters where each
+//! evaluation is an awaitable (simulated HTTP) call.
+//!
+//! Demonstrates:
+//! - Implementing [`AsyncProblem`].
+//! - Driving the optimizer through `tokio` with bounded concurrency.
+//! - Comparing wall-clock time at concurrency = 1 vs 8.
+//!
+//! Run with: `cargo run --release --features async --example async_eval`
+
+use std::time::Instant;
+
+use heuropt::core::async_problem::AsyncProblem;
+use heuropt::prelude::*;
+
+struct RemoteService;
+
+impl AsyncProblem for RemoteService {
+    type Decision = Vec<f64>;
+
+    fn objectives(&self) -> ObjectiveSpace {
+        ObjectiveSpace::new(vec![Objective::minimize("loss")])
+    }
+
+    async fn evaluate_async(&self, x: &Vec<f64>) -> Evaluation {
+        // Simulate a 20 ms remote-service round-trip per evaluation.
+        // The compute itself is ~free; the latency is the bottleneck.
+        tokio::time::sleep(std::time::Duration::from_millis(20)).await;
+        let loss: f64 = x.iter().map(|v| v * v).sum();
+        Evaluation::new(vec![loss])
+    }
+}
+
+#[tokio::main]
+async fn main() {
+    let bounds = vec![(-1.0_f64, 1.0_f64); 4];
+    let problem = RemoteService;
+
+    println!("RandomSearch with 200 evaluations (20 ms each)");
+    println!();
+
+    for &concurrency in &[1_usize, 4, 16] {
+        let mut opt = RandomSearch::new(
+            RandomSearchConfig {
+                iterations: 100,
+                batch_size: 2,
+                seed: 42,
+            },
+            RealBounds::new(bounds.clone()),
+        );
+        let started = Instant::now();
+        let result = opt.run_async(&problem, concurrency).await;
+        let elapsed = started.elapsed();
+        println!(
+            "concurrency = {:>2}  elapsed = {:>5} ms  best loss = {:>8.5}  evaluations = {}",
+            concurrency,
+            elapsed.as_millis(),
+            result.best.unwrap().evaluation.objectives[0],
+            result.evaluations,
+        );
+    }
+
+    println!();
+    println!("DifferentialEvolution at concurrency=8");
+    let started = Instant::now();
+    let mut de = DifferentialEvolution::new(
+        DifferentialEvolutionConfig {
+            population_size: 8,
+            generations: 10,
+            differential_weight: 0.5,
+            crossover_probability: 0.9,
+            seed: 42,
+        },
+        RealBounds::new(bounds.clone()),
+    );
+    let result = de.run_async(&problem, 8).await;
+    let elapsed = started.elapsed();
+    println!(
+        "elapsed = {:>5} ms  best loss = {:>8.5}  evaluations = {}",
+        elapsed.as_millis(),
+        result.best.unwrap().evaluation.objectives[0],
+        result.evaluations,
+    );
+}

examples/constrained.rs

@@ -0,0 +1,125 @@
+//! Constrained multi-objective optimization (BNH problem) plus a
+//! demo of the observer / stop-condition API.
+//!
+//! BNH (Binh & Korn 1996) is a 2-variable / 2-objective / 2-constraint
+//! multi-objective problem:
+//!
+//! ```text
+//! minimize  f1 = 4·x1² + 4·x2²
+//!           f2 = (x1 − 5)² + (x2 − 5)²
+//! subject to
+//!           g1: (x1 − 5)² + x2² ≤ 25
+//!           g2: (x1 − 8)² + (x2 + 3)² ≥ 7.7
+//! 0 ≤ x1 ≤ 5,  0 ≤ x2 ≤ 3
+//! ```
+//!
+//! Demonstrates:
+//! - Constraint handling via `Evaluation::constrained` (heuropt's
+//!   default tournament/Pareto comparators prefer feasibles).
+//! - The Observer API: a `Stagnation` observer that halts the run
+//!   once the front stops improving, plus a `Periodic` observer that
+//!   prints progress every 25 generations.
+//! - Composing observers with `.or()`.
+//!
+//! Run with: `cargo run --release --example constrained`
+
+use heuropt::prelude::*;
+
+struct Bnh;
+
+impl Problem for Bnh {
+    type Decision = Vec<f64>;
+
+    fn objectives(&self) -> ObjectiveSpace {
+        ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
+    }
+
+    fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
+        let f1 = 4.0 * x[0] * x[0] + 4.0 * x[1] * x[1];
+        let f2 = (x[0] - 5.0).powi(2) + (x[1] - 5.0).powi(2);
+
+        // g1: (x1 − 5)² + x2² ≤ 25  → violation = max(0, lhs − 25)
+        let g1 = ((x[0] - 5.0).powi(2) + x[1].powi(2) - 25.0).max(0.0);
+        // g2: (x1 − 8)² + (x2 + 3)² ≥ 7.7  → violation = max(0, 7.7 − lhs)
+        let g2 = (7.7 - ((x[0] - 8.0).powi(2) + (x[1] + 3.0).powi(2))).max(0.0);
+
+        let total_violation = g1 + g2;
+        Evaluation::constrained(vec![f1, f2], total_violation)
+    }
+}
+
+fn main() {
+    let bounds = vec![(0.0_f64, 5.0_f64), (0.0_f64, 3.0_f64)];
+
+    // Compose stop conditions: halt after 5 s OR (via .or()) print
+    // periodic progress every 25 generations. The Periodic observer
+    // never breaks; it only logs.
+    let stop = MaxTime::new(std::time::Duration::from_secs(5));
+    let progress = Periodic::new(25, |snap: &Snapshot<'_, Vec<f64>>| {
+        let feasible_in_pop = snap
+            .population
+            .iter()
+            .filter(|c| c.evaluation.is_feasible())
+            .count();
+        let front_size = snap.pareto_front.map(|f| f.len()).unwrap_or(0);
+        println!(
+            "gen {:>4}  evaluations = {:>6}  feasible/pop = {}/{}  front = {}",
+            snap.iteration,
+            snap.evaluations,
+            feasible_in_pop,
+            snap.population.len(),
+            front_size,
+        );
+    });
+    let mut observer = <_ as Observer<Vec<f64>>>::or(stop, progress);
+
+    let mut opt = Nsga2::new(
+        Nsga2Config {
+            population_size: 100,
+            generations: 250,
+            seed: 42,
+        },
+        RealBounds::new(bounds.clone()),
+        CompositeVariation {
+            crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
+            mutation: PolynomialMutation::new(bounds, 20.0, 1.0 / 2.0),
+        },
+    );
+    let result = opt.run_with(&Bnh, &mut observer);
+
+    let total_feasible = result
+        .population
+        .iter()
+        .filter(|c| c.evaluation.is_feasible())
+        .count();
+
+    println!();
+    println!("Final state after {} generations:", result.generations);
+    println!("  total evaluations:        {}", result.evaluations);
+    println!(
+        "  feasible / total pop:     {} / {}",
+        total_feasible,
+        result.population.len()
+    );
+    println!("  pareto front size:        {}", result.pareto_front.len());
+    println!();
+    println!("Sample of the front (f1, f2):");
+    let mut sorted = result.pareto_front.clone();
+    sorted.sort_by(|a, b| {
+        a.evaluation.objectives[0]
+            .partial_cmp(&b.evaluation.objectives[0])
+            .unwrap_or(std::cmp::Ordering::Equal)
+    });
+    let n = sorted.len();
+    if n > 0 {
+        for k in (0..n).step_by((n / 5).max(1)) {
+            let c = &sorted[k];
+            println!(
+                "  f1 = {:>7.3}, f2 = {:>7.3}, violation = {:.3}",
+                c.evaluation.objectives[0],
+                c.evaluation.objectives[1],
+                c.evaluation.constraint_violation,
+            );
+        }
+    }
+}

src/algorithms/differential_evolution.rs

@@ -3,7 +3,6 @@
 use rand::Rng as _;
 
 use crate::algorithms::parallel_eval::evaluate_batch;
-use crate::core::candidate::Candidate;
 use crate::core::objective::Direction;
 use crate::core::population::Population;
 use crate::core::problem::Problem;
@@ -95,6 +94,16 @@ where
     P: Problem<Decision = Vec<f64>> + Sync,
 {
     fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
+        self.run_with(problem, &mut ())
+    }
+
+    fn run_with<O>(&mut self, problem: &P, observer: &mut O) -> OptimizationResult<P::Decision>
+    where
+        O: crate::observer::Observer<P::Decision>,
+    {
+        use crate::observer::Snapshot;
+        use std::ops::ControlFlow;
+
         assert!(
             self.config.population_size >= 4,
             "DifferentialEvolution requires population_size >= 4 (DE/rand/1 needs three distinct donors plus the target)",
@@ -110,6 +119,7 @@ where
             "DifferentialEvolution only supports single-objective problems",
         );
         let direction = objectives.objectives[0].direction;
+        let started = std::time::Instant::now();
 
         let dim = self.bounds.bounds.len();
         let n = self.config.population_size;
@@ -122,12 +132,39 @@ where
         };
         let initial_pop = evaluate_batch(problem, decisions.clone());
         let mut evaluations = initial_pop.len();
-        let mut evals: Vec<f64> = initial_pop
+        let mut current_pop = initial_pop;
+        let mut evals: Vec<f64> = current_pop
             .iter()
             .map(|c| c.evaluation.objectives[0])
             .collect();
+        let mut completed_generations: usize = 0;
 
-        for _gen in 0..self.config.generations {
+        // Initial snapshot.
+        {
+            let best = best_candidate(&current_pop, &objectives);
+            let snap = Snapshot {
+                iteration: 0,
+                evaluations,
+                elapsed: started.elapsed(),
+                population: &current_pop,
+                pareto_front: None,
+                best: best.as_ref(),
+                objectives: &objectives,
+            };
+            if let ControlFlow::Break(()) = observer.observe(&snap) {
+                let front = pareto_front(&current_pop, &objectives);
+                let best = best_candidate(&current_pop, &objectives);
+                return OptimizationResult::new(
+                    Population::new(current_pop),
+                    front,
+                    best,
+                    evaluations,
+                    completed_generations,
+                );
+            }
+        }
+
+        for generation in 1..=self.config.generations {
             // Phase 1 (serial): construct one trial per target. RNG state is
             // consumed in deterministic order so seeded runs reproduce
             // exactly regardless of the `parallel` feature.
@@ -164,18 +201,135 @@ where
                     Direction::Maximize => trial_obj >= target_obj,
                 };
                 if trial_better {
-                    decisions[i] = trial_cand.decision;
+                    decisions[i] = trial_cand.decision.clone();
                     evals[i] = trial_obj;
+                    current_pop[i] = trial_cand;
+                }
+            }
+            completed_generations = generation;
+
+            // Per-generation snapshot.
+            let best = best_candidate(&current_pop, &objectives);
+            let snap = Snapshot {
+                iteration: generation,
+                evaluations,
+                elapsed: started.elapsed(),
+                population: &current_pop,
+                pareto_front: None,
+                best: best.as_ref(),
+                objectives: &objectives,
+            };
+            if let ControlFlow::Break(()) = observer.observe(&snap) {
+                break;
+            }
+        }
+
+        // Re-evaluate to make sure final population is consistent (current_pop is already current).
+        let front = pareto_front(&current_pop, &objectives);
+        let best = best_candidate(&current_pop, &objectives);
+        OptimizationResult::new(
+            Population::new(current_pop),
+            front,
+            best,
+            evaluations,
+            completed_generations,
+        )
+    }
+}
+
+#[cfg(feature = "async")]
+impl DifferentialEvolution {
+    /// Async version of [`Optimizer::run`] — drives evaluations through
+    /// the user-chosen async runtime. Available only with the `async`
+    /// feature.
+    ///
+    /// `concurrency` bounds in-flight evaluations per batch (initial
+    /// population and per-generation trials).
+    pub async fn run_async<P>(
+        &mut self,
+        problem: &P,
+        concurrency: usize,
+    ) -> OptimizationResult<Vec<f64>>
+    where
+        P: crate::core::async_problem::AsyncProblem<Decision = Vec<f64>>,
+    {
+        use rand::Rng as _;
+
+        use crate::algorithms::parallel_eval_async::evaluate_batch_async;
+        use crate::core::candidate::Candidate;
+        use crate::traits::Initializer as _;
+
+        assert!(
+            self.config.population_size >= 4,
+            "DifferentialEvolution requires population_size >= 4",
+        );
+        assert!(
+            (0.0..=1.0).contains(&self.config.crossover_probability),
+            "DifferentialEvolution crossover_probability must be in [0.0, 1.0]",
+        );
+
+        let objectives = problem.objectives();
+        assert!(
+            objectives.is_single_objective(),
+            "DifferentialEvolution only supports single-objective problems",
+        );
+        let direction = objectives.objectives[0].direction;
+
+        let dim = self.bounds.bounds.len();
+        let n = self.config.population_size;
+        let mut rng = rng_from_seed(self.config.seed);
+
+        let mut decisions: Vec<Vec<f64>> = self.bounds.initialize(n, &mut rng);
+        let initial_pop = evaluate_batch_async(problem, decisions.clone(), concurrency).await;
+        let mut evaluations = initial_pop.len();
+        let mut current_pop = initial_pop;
+        let mut evals: Vec<f64> = current_pop
+            .iter()
+            .map(|c| c.evaluation.objectives[0])
+            .collect();
+
+        for _generation in 0..self.config.generations {
+            let trials: Vec<Vec<f64>> = (0..n)
+                .map(|i| {
+                    let (r1, r2, r3) = pick_three_distinct(n, i, &mut rng);
+                    let j_rand = rng.random_range(0..dim);
+                    let mut trial = decisions[i].clone();
+                    for j in 0..dim {
+                        let take_donor =
+                            rng.random_bool(self.config.crossover_probability) || j == j_rand;
+                        if take_donor {
+                            let mutant = decisions[r1][j]
+                                + self.config.differential_weight
+                                    * (decisions[r2][j] - decisions[r3][j]);
+                            let (lo, hi) = self.bounds.bounds[j];
+                            trial[j] = mutant.clamp(lo, hi);
+                        }
+                    }
+                    trial
+                })
+                .collect();
+            let trial_cands: Vec<Candidate<Vec<f64>>> =
+                evaluate_batch_async(problem, trials, concurrency).await;
+            evaluations += trial_cands.len();
+            for (i, trial_cand) in trial_cands.into_iter().enumerate() {
+                let trial_obj = trial_cand.evaluation.objectives[0];
+                let target_obj = evals[i];
+                let trial_better = match direction {
+                    crate::core::objective::Direction::Minimize => trial_obj <= target_obj,
+                    crate::core::objective::Direction::Maximize => trial_obj >= target_obj,
+                };
+                if trial_better {
+                    decisions[i] = trial_cand.decision.clone();
+                    evals[i] = trial_obj;
+                    current_pop[i] = trial_cand;
                 }
             }
         }
 
-        let final_pop: Vec<Candidate<Vec<f64>>> = evaluate_batch(problem, decisions);
+        let front = pareto_front(&current_pop, &objectives);
-        evaluations += final_pop.len();
+        let best = best_candidate(&current_pop, &objectives);
-        let front = pareto_front(&final_pop, &objectives);
-        let best = best_candidate(&final_pop, &objectives);
         OptimizationResult::new(
-            Population::new(final_pop),
+            Population::new(current_pop),
             front,
             best,
             evaluations,

src/algorithms/mod.rs

@@ -22,6 +22,8 @@ pub mod nsga3;
 pub mod one_plus_one_es;
 pub mod paes;
 pub(crate) mod parallel_eval;
+#[cfg(feature = "async")]
+pub(crate) mod parallel_eval_async;
 pub mod particle_swarm;
 pub mod pesa2;
 pub mod random_search;

src/algorithms/nsga2.rs

@@ -108,6 +108,16 @@ where
     V: Variation<P::Decision>,
 {
     fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
+        self.run_with(problem, &mut ())
+    }
+
+    fn run_with<O>(&mut self, problem: &P, observer: &mut O) -> OptimizationResult<P::Decision>
+    where
+        O: crate::observer::Observer<P::Decision>,
+    {
+        use crate::observer::Snapshot;
+        use std::ops::ControlFlow;
+
         assert!(
             self.config.population_size > 0,
             "Nsga2 population_size must be greater than 0",
@@ -115,6 +125,7 @@ where
         let n = self.config.population_size;
         let objectives = problem.objectives();
         let mut rng = rng_from_seed(self.config.seed);
+        let started = std::time::Instant::now();
 
         // Initial population.
         let initial_decisions = self.initializer.initialize(n, &mut rng);
@@ -130,7 +141,27 @@ where
         // round of tournament selection has data to compare on.
         let mut annotated = annotate(population, &objectives);
 
-        for _ in 0..self.config.generations {
+        // Observer: notify after the initial population.
+        let mut completed_generations: usize = 0;
+        let pop_view: Vec<Candidate<P::Decision>> =
+            annotated.iter().map(|e| e.candidate.clone()).collect();
+        let front_view = pareto_front(&pop_view, &objectives);
+        let snap = Snapshot {
+            iteration: 0,
+            evaluations,
+            elapsed: started.elapsed(),
+            population: &pop_view,
+            pareto_front: Some(&front_view),
+            best: None,
+            objectives: &objectives,
+        };
+        if let ControlFlow::Break(()) = observer.observe(&snap) {
+            return finalize_nsga2(annotated, &objectives, evaluations, completed_generations);
+        }
+        drop(pop_view);
+        drop(front_view);
+
+        for generation in 1..=self.config.generations {
             // --- Phase 1: serial parent selection + variation ---
             let mut offspring_decisions: Vec<P::Decision> = Vec::with_capacity(n);
             while offspring_decisions.len() < n {
@@ -189,23 +220,48 @@ where
                 }
             }
             annotated = annotate(next, &objectives);
+            completed_generations = generation;
+
+            // Per-generation observation.
+            let pop_view: Vec<Candidate<P::Decision>> =
+                annotated.iter().map(|e| e.candidate.clone()).collect();
+            let front_view = pareto_front(&pop_view, &objectives);
+            let snap = Snapshot {
+                iteration: generation,
+                evaluations,
+                elapsed: started.elapsed(),
+                population: &pop_view,
+                pareto_front: Some(&front_view),
+                best: None,
+                objectives: &objectives,
+            };
+            if let ControlFlow::Break(()) = observer.observe(&snap) {
+                return finalize_nsga2(annotated, &objectives, evaluations, completed_generations);
+            }
         }
 
-        // Return final state.
+        finalize_nsga2(annotated, &objectives, evaluations, self.config.generations)
-        let final_pop: Vec<Candidate<P::Decision>> =
-            annotated.into_iter().map(|e| e.candidate).collect();
-        let front = pareto_front(&final_pop, &objectives);
-        let best = best_candidate(&final_pop, &objectives);
-        OptimizationResult::new(
-            Population::new(final_pop),
-            front,
-            best,
-            evaluations,
-            self.config.generations,
-        )
     }
 }
 
+fn finalize_nsga2<D: Clone>(
+    annotated: Vec<Nsga2Entry<D>>,
+    objectives: &crate::core::objective::ObjectiveSpace,
+    evaluations: usize,
+    generations: usize,
+) -> OptimizationResult<D> {
+    let final_pop: Vec<Candidate<D>> = annotated.into_iter().map(|e| e.candidate).collect();
+    let front = pareto_front(&final_pop, objectives);
+    let best = best_candidate(&final_pop, objectives);
+    OptimizationResult::new(
+        Population::new(final_pop),
+        front,
+        best,
+        evaluations,
+        generations,
+    )
+}
+
 fn annotate<D: Clone>(
     population: Vec<Candidate<D>>,
     objectives: &crate::core::objective::ObjectiveSpace,

src/algorithms/parallel_eval_async.rs

@@ -0,0 +1,58 @@
+//! Async population evaluator.
+//!
+//! Available only with the `async` feature. Used by the `run_async`
+//! method on algorithms that support async problems.
+
+use futures::stream::{FuturesOrdered, StreamExt};
+
+use crate::core::async_problem::AsyncProblem;
+use crate::core::candidate::Candidate;
+
+/// Evaluate every decision concurrently against `problem`, preserving
+/// input order in the returned vector. Concurrency is bounded by
+/// `concurrency` (≥ 1) — too high a value wastes memory and may
+/// overload downstream services; too low forfeits parallelism.
+///
+/// Returns a future that the caller drives via their preferred
+/// runtime (typically tokio).
+pub async fn evaluate_batch_async<P>(
+    problem: &P,
+    decisions: Vec<P::Decision>,
+    concurrency: usize,
+) -> Vec<Candidate<P::Decision>>
+where
+    P: AsyncProblem,
+{
+    assert!(
+        concurrency >= 1,
+        "evaluate_batch_async concurrency must be >= 1"
+    );
+    let mut out: Vec<Candidate<P::Decision>> = Vec::with_capacity(decisions.len());
+
+    // Process in concurrency-bounded chunks to keep peak memory low
+    // and avoid blasting downstream services. Each chunk uses
+    // FuturesOrdered to preserve per-chunk order, and chunks are
+    // emitted in their natural order.
+    let mut iter = decisions.into_iter();
+    loop {
+        let mut futs = FuturesOrdered::new();
+        for _ in 0..concurrency {
+            match iter.next() {
+                Some(d) => {
+                    futs.push_back(async move {
+                        let e = problem.evaluate_async(&d).await;
+                        Candidate::new(d, e)
+                    });
+                }
+                None => break,
+            }
+        }
+        if futs.is_empty() {
+            break;
+        }
+        while let Some(c) = futs.next().await {
+            out.push(c);
+        }
+    }
+    out
+}

src/algorithms/random_search.rs

@@ -88,19 +88,85 @@ where
     I: Initializer<P::Decision>,
 {
     fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
+        self.run_with(problem, &mut ())
+    }
+
+    fn run_with<O>(&mut self, problem: &P, observer: &mut O) -> OptimizationResult<P::Decision>
+    where
+        O: crate::observer::Observer<P::Decision>,
+    {
+        use crate::observer::Snapshot;
+        use std::ops::ControlFlow;
+
         let objectives = problem.objectives();
         let mut rng = rng_from_seed(self.config.seed);
         let mut all: Vec<Candidate<P::Decision>> = Vec::new();
         let mut evaluations = 0usize;
+        let started = std::time::Instant::now();
+        let mut completed: usize = 0;
 
-        for _ in 0..self.config.iterations {
+        for iteration in 1..=self.config.iterations {
             let decisions = self
                 .initializer
                 .initialize(self.config.batch_size, &mut rng);
             evaluations += decisions.len();
             all.extend(evaluate_batch(problem, decisions));
+            completed = iteration;
+
+            let best = best_candidate(&all, &objectives);
+            let snap = Snapshot {
+                iteration,
+                evaluations,
+                elapsed: started.elapsed(),
+                population: &all,
+                pareto_front: None,
+                best: best.as_ref(),
+                objectives: &objectives,
+            };
+            if let ControlFlow::Break(()) = observer.observe(&snap) {
+                break;
+            }
         }
 
+        let front = pareto_front(&all, &objectives);
+        let best = best_candidate(&all, &objectives);
+        OptimizationResult::new(Population::new(all), front, best, evaluations, completed)
+    }
+}
+
+#[cfg(feature = "async")]
+impl<I> RandomSearch<I> {
+    /// Async version of [`Optimizer::run`] — drives evaluations through
+    /// the user-chosen async runtime (typically tokio). Useful when
+    /// `evaluate` is IO-bound (HTTP, RPC, subprocess).
+    ///
+    /// `concurrency` bounds how many evaluations are in-flight at once;
+    /// `1` is sequential, larger values push more load to the
+    /// downstream service.
+    ///
+    /// Available only with the `async` feature.
+    pub async fn run_async<P>(
+        &mut self,
+        problem: &P,
+        concurrency: usize,
+    ) -> OptimizationResult<P::Decision>
+    where
+        P: crate::core::async_problem::AsyncProblem,
+        I: Initializer<P::Decision>,
+    {
+        use crate::algorithms::parallel_eval_async::evaluate_batch_async;
+        let objectives = problem.objectives();
+        let mut rng = rng_from_seed(self.config.seed);
+        let mut all: Vec<Candidate<P::Decision>> = Vec::new();
+        let mut evaluations = 0usize;
+        for _ in 0..self.config.iterations {
+            let decisions = self
+                .initializer
+                .initialize(self.config.batch_size, &mut rng);
+            evaluations += decisions.len();
+            let cands = evaluate_batch_async(problem, decisions, concurrency).await;
+            all.extend(cands);
+        }
         let front = pareto_front(&all, &objectives);
         let best = best_candidate(&all, &objectives);
         OptimizationResult::new(

src/core/async_problem.rs

@@ -0,0 +1,54 @@
+//! Async-evaluable problems for IO-bound workloads.
+//!
+//! Most heuropt algorithms operate synchronously: their `Problem::evaluate`
+//! returns immediately. For workloads where evaluation is *IO-bound* — calling
+//! an HTTP service, querying a remote model, spawning a subprocess —
+//! awaiting an async fn is much more efficient than blocking a worker
+//! thread.
+//!
+//! [`AsyncProblem`] mirrors [`Problem`](crate::core::Problem) but its
+//! `evaluate_async` returns a future. Algorithms that support async
+//! evaluation (NSGA-II, DE, RandomSearch as of v0.7.0; others land
+//! incrementally) expose a `run_async` method that drives evaluations
+//! through a user-chosen async runtime (typically tokio).
+//!
+//! Available only with the `async` feature.
+
+use std::future::Future;
+
+use crate::core::evaluation::Evaluation;
+use crate::core::objective::ObjectiveSpace;
+
+/// A problem whose evaluation is async — useful when `evaluate` does
+/// IO (HTTP, RPC, subprocess) rather than pure CPU work.
+///
+/// Mirrors [`Problem`](crate::core::Problem) one-for-one except that
+/// `evaluate_async` returns a future. The returned future must be
+/// `Send` so the algorithm can run many evaluations concurrently
+/// across a runtime's worker pool.
+///
+/// Implementors who already have a synchronous `Problem` can adapt
+/// to `AsyncProblem` with a one-line wrapper:
+///
+/// ```ignore
+/// impl AsyncProblem for MyProblem {
+///     type Decision = <Self as Problem>::Decision;
+///     fn objectives(&self) -> ObjectiveSpace { Problem::objectives(self) }
+///     async fn evaluate_async(&self, x: &Self::Decision) -> Evaluation {
+///         Problem::evaluate(self, x)
+///     }
+/// }
+/// ```
+pub trait AsyncProblem: Sync {
+    /// The thing the optimizer changes. Same constraints as
+    /// [`Problem::Decision`](crate::core::Problem::Decision).
+    type Decision: Clone + Send + Sync;
+
+    /// Return the objectives for this problem.
+    fn objectives(&self) -> ObjectiveSpace;
+
+    /// Evaluate `decision` asynchronously. The returned future is
+    /// driven by whichever runtime the algorithm's `run_async` is
+    /// invoked from.
+    fn evaluate_async(&self, decision: &Self::Decision) -> impl Future<Output = Evaluation> + Send;
+}

src/core/mod.rs

@@ -1,5 +1,7 @@
 //! Concrete data types and the `Problem` trait that the rest of the crate is built on.
 
+#[cfg(feature = "async")]
+pub mod async_problem;
 pub mod candidate;
 pub mod evaluation;
 pub mod objective;
@@ -9,6 +11,8 @@ pub mod problem;
 pub mod result;
 pub mod rng;
 
+#[cfg(feature = "async")]
+pub use async_problem::AsyncProblem;
 pub use candidate::*;
 pub use evaluation::*;
 pub use objective::*;

src/core/population.rs

@@ -34,6 +34,11 @@ impl<D> Population<D> {
         self.candidates.iter()
     }
 
+    /// View the candidates as a slice.
+    pub fn as_slice(&self) -> &[Candidate<D>] {
+        &self.candidates
+    }
+
     /// Unwrap into the inner `Vec<Candidate<D>>`.
     pub fn into_vec(self) -> Vec<Candidate<D>> {
         self.candidates

src/lib.rs

@@ -68,6 +68,7 @@ pub mod algorithms;
 pub mod core;
 pub(crate) mod internal;
 pub mod metrics;
+pub mod observer;
 pub mod operators;
 pub mod pareto;
 pub mod prelude;

src/metrics/igd.rs

@@ -0,0 +1,207 @@
+//! Inverted Generational Distance (IGD) and IGD+ performance indicators.
+//!
+//! Both quantify how well an approximation set covers a reference set
+//! (typically the true Pareto front). Smaller values are better.
+
+use crate::core::candidate::Candidate;
+use crate::core::evaluation::Evaluation;
+use crate::core::objective::ObjectiveSpace;
+
+/// Inverted Generational Distance.
+///
+/// For each point in the `reference` set, compute the Euclidean distance
+/// to its nearest neighbor in the `approximation` set (in minimization-
+/// oriented objective space), then average:
+///
+/// ```text
+/// IGD(A) = (1 / |R|) · Σ_{r ∈ R} min_{a ∈ A} ‖a − r‖₂
+/// ```
+///
+/// Lower is better. IGD captures both convergence (close to the front)
+/// and spread (the approximation must cover the reference).
+///
+/// # Panics
+///
+/// If `reference` is empty.
+///
+/// # Example
+///
+/// ```
+/// use heuropt::prelude::*;
+/// use heuropt::metrics::igd::igd;
+///
+/// let space = ObjectiveSpace::new(vec![
+///     Objective::minimize("f1"),
+///     Objective::minimize("f2"),
+/// ]);
+/// // Approximation: a sparse 2-point front.
+/// let approx = [
+///     Candidate::new((), Evaluation::new(vec![0.0, 1.0])),
+///     Candidate::new((), Evaluation::new(vec![1.0, 0.0])),
+/// ];
+/// // Reference: a dense 3-point sample of the true front.
+/// let reference = [
+///     Evaluation::new(vec![0.0, 1.0]),
+///     Evaluation::new(vec![0.5, 0.5]),
+///     Evaluation::new(vec![1.0, 0.0]),
+/// ];
+/// let v = igd(&approx, &reference, &space);
+/// // The middle reference point is unfortunately distance √(0.5²+0.5²) = 0.707
+/// // from each approximation point; the boundary points are 0 away.
+/// // IGD = (0 + 0.707 + 0) / 3 ≈ 0.236.
+/// assert!((v - 0.2357).abs() < 1e-3);
+/// ```
+pub fn igd<D>(
+    approximation: &[Candidate<D>],
+    reference: &[Evaluation],
+    objectives: &ObjectiveSpace,
+) -> f64 {
+    assert!(
+        !reference.is_empty(),
+        "igd: reference set must not be empty"
+    );
+    let approx_oriented: Vec<Vec<f64>> = approximation
+        .iter()
+        .map(|c| objectives.as_minimization(&c.evaluation.objectives))
+        .collect();
+    if approx_oriented.is_empty() {
+        return f64::INFINITY;
+    }
+    let mut total = 0.0_f64;
+    for r in reference {
+        let r_oriented = objectives.as_minimization(&r.objectives);
+        let mut min_d = f64::INFINITY;
+        for a in &approx_oriented {
+            let d: f64 = a
+                .iter()
+                .zip(r_oriented.iter())
+                .map(|(x, y)| (x - y).powi(2))
+                .sum::<f64>()
+                .sqrt();
+            if d < min_d {
+                min_d = d;
+            }
+        }
+        total += min_d;
+    }
+    total / reference.len() as f64
+}
+
+/// IGD+ — a dominance-respecting variant of IGD.
+///
+/// For each reference point `r`, the distance to an approximation
+/// point `a` is computed only on objectives where `a` is *worse than*
+/// `r` — i.e. on the "violation" component of the gap. This makes
+/// IGD+ a Pareto-compliant indicator: adding a dominated point to the
+/// approximation never improves the score.
+///
+/// ```text
+/// IGD+(A) = (1 / |R|) · Σ_{r ∈ R} min_{a ∈ A} ‖max(a − r, 0)‖₂
+/// ```
+///
+/// Lower is better.
+///
+/// # Panics
+///
+/// If `reference` is empty.
+pub fn igd_plus<D>(
+    approximation: &[Candidate<D>],
+    reference: &[Evaluation],
+    objectives: &ObjectiveSpace,
+) -> f64 {
+    assert!(
+        !reference.is_empty(),
+        "igd_plus: reference set must not be empty"
+    );
+    let approx_oriented: Vec<Vec<f64>> = approximation
+        .iter()
+        .map(|c| objectives.as_minimization(&c.evaluation.objectives))
+        .collect();
+    if approx_oriented.is_empty() {
+        return f64::INFINITY;
+    }
+    let mut total = 0.0_f64;
+    for r in reference {
+        let r_oriented = objectives.as_minimization(&r.objectives);
+        let mut min_d = f64::INFINITY;
+        for a in &approx_oriented {
+            let d: f64 = a
+                .iter()
+                .zip(r_oriented.iter())
+                .map(|(x, y)| (x - y).max(0.0).powi(2))
+                .sum::<f64>()
+                .sqrt();
+            if d < min_d {
+                min_d = d;
+            }
+        }
+        total += min_d;
+    }
+    total / reference.len() as f64
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::core::objective::Objective;
+
+    fn space_min2() -> ObjectiveSpace {
+        ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
+    }
+
+    fn cand(obj: Vec<f64>) -> Candidate<()> {
+        Candidate::new((), Evaluation::new(obj))
+    }
+
+    #[test]
+    fn igd_perfect_match_is_zero() {
+        let s = space_min2();
+        let approx = [cand(vec![0.0, 1.0]), cand(vec![1.0, 0.0])];
+        let reference = [
+            Evaluation::new(vec![0.0, 1.0]),
+            Evaluation::new(vec![1.0, 0.0]),
+        ];
+        let v = igd(&approx, &reference, &s);
+        assert!(v < 1e-12);
+    }
+
+    #[test]
+    fn igd_known_value() {
+        let s = space_min2();
+        let approx = [cand(vec![0.0, 0.0])];
+        let reference = [Evaluation::new(vec![1.0, 1.0])];
+        let v = igd(&approx, &reference, &s);
+        assert!((v - 2.0_f64.sqrt()).abs() < 1e-12);
+    }
+
+    #[test]
+    fn igd_plus_dominated_point_does_not_improve() {
+        let s = space_min2();
+        let reference = [
+            Evaluation::new(vec![0.0, 1.0]),
+            Evaluation::new(vec![1.0, 0.0]),
+        ];
+        let base = vec![cand(vec![0.5, 0.5])];
+        let with_dominated = vec![cand(vec![0.5, 0.5]), cand(vec![1.0, 1.0])];
+        let v_base = igd_plus(&base, &reference, &s);
+        let v_with = igd_plus(&with_dominated, &reference, &s);
+        // Adding a dominated point should not improve the score.
+        assert!(v_with >= v_base - 1e-12);
+    }
+
+    #[test]
+    fn igd_empty_approximation_is_infinity() {
+        let s = space_min2();
+        let approx: [Candidate<()>; 0] = [];
+        let reference = [Evaluation::new(vec![0.0, 1.0])];
+        assert!(igd(&approx, &reference, &s).is_infinite());
+    }
+
+    #[test]
+    #[should_panic(expected = "reference set must not be empty")]
+    fn igd_empty_reference_panics() {
+        let s = space_min2();
+        let approx = [cand(vec![0.0, 1.0])];
+        let _ = igd::<()>(&approx, &[], &s);
+    }
+}

src/metrics/mod.rs

@@ -1,7 +1,11 @@
 //! Quality metrics for Pareto fronts.
 
 pub mod hypervolume;
+pub mod igd;
+pub mod r2;
 pub mod spacing;
 
 pub use hypervolume::*;
+pub use igd::{igd, igd_plus};
+pub use r2::r2;
 pub use spacing::*;

src/metrics/r2.rs

@@ -0,0 +1,173 @@
+//! R2 indicator — a unary quality measure for Pareto fronts.
+//!
+//! For each weight vector `λ` in a user-supplied set, find the
+//! best (smallest) weighted Tchebycheff value across the front;
+//! average over all weight vectors. Lower is better.
+
+use crate::core::candidate::Candidate;
+use crate::core::objective::ObjectiveSpace;
+
+/// R2 indicator using the weighted Tchebycheff utility.
+///
+/// ```text
+/// R2(A) = (1 / |Λ|) · Σ_{λ ∈ Λ} min_{a ∈ A} max_i { λ_i · |a_i − z*_i| }
+/// ```
+///
+/// where `z*` is the ideal point (per-axis minimum across the
+/// approximation, in minimization-oriented coordinates) and `Λ` is
+/// a set of unit-simplex weight vectors. Lower is better.
+///
+/// Use [`das_dennis`](crate::pareto::das_dennis) to generate the
+/// canonical structured weight set.
+///
+/// # Panics
+///
+/// If the approximation is empty, or any weight vector has wrong
+/// length / negative entries / zero sum.
+///
+/// # Example
+///
+/// ```
+/// use heuropt::prelude::*;
+/// use heuropt::metrics::r2::r2;
+///
+/// let space = ObjectiveSpace::new(vec![
+///     Objective::minimize("f1"),
+///     Objective::minimize("f2"),
+/// ]);
+/// let approx = [
+///     Candidate::new((), Evaluation::new(vec![0.0, 1.0])),
+///     Candidate::new((), Evaluation::new(vec![1.0, 0.0])),
+/// ];
+/// // Two weight vectors: (1, 0) and (0, 1) — extreme directions.
+/// let weights = [vec![1.0, 0.0], vec![0.0, 1.0]];
+/// let v = r2(&approx, &weights, &space);
+/// // For each direction, the best front member matches that axis exactly.
+/// // R2 = 0 since the ideal point is achieved on each direction.
+/// assert!(v < 1e-12);
+/// ```
+pub fn r2<D>(
+    approximation: &[Candidate<D>],
+    weights: &[Vec<f64>],
+    objectives: &ObjectiveSpace,
+) -> f64 {
+    assert!(
+        !approximation.is_empty(),
+        "r2: approximation must not be empty"
+    );
+    assert!(!weights.is_empty(), "r2: weight set must not be empty");
+    let m = objectives.len();
+    for (i, w) in weights.iter().enumerate() {
+        assert_eq!(
+            w.len(),
+            m,
+            "r2: weight {i} has wrong length ({} vs {m})",
+            w.len()
+        );
+        assert!(
+            w.iter().all(|&v| v >= 0.0),
+            "r2: weight {i} has a negative entry"
+        );
+        assert!(w.iter().sum::<f64>() > 0.0, "r2: weight {i} has zero sum");
+    }
+
+    // Convert all approximation members to minimization orientation once.
+    let oriented: Vec<Vec<f64>> = approximation
+        .iter()
+        .map(|c| objectives.as_minimization(&c.evaluation.objectives))
+        .collect();
+
+    // Ideal point z* (per-axis minimum).
+    let mut z_star = vec![f64::INFINITY; m];
+    for o in &oriented {
+        for k in 0..m {
+            if o[k] < z_star[k] {
+                z_star[k] = o[k];
+            }
+        }
+    }
+
+    let mut total = 0.0_f64;
+    for w in weights {
+        let mut best = f64::INFINITY;
+        for o in &oriented {
+            // Weighted Tchebycheff: max_i { w_i · |o_i − z*_i| }
+            let mut t = 0.0_f64;
+            for k in 0..m {
+                let dk = (o[k] - z_star[k]).abs() * w[k];
+                if dk > t {
+                    t = dk;
+                }
+            }
+            if t < best {
+                best = t;
+            }
+        }
+        total += best;
+    }
+    total / weights.len() as f64
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::core::evaluation::Evaluation;
+    use crate::core::objective::Objective;
+    use crate::pareto::das_dennis;
+
+    fn space_min2() -> ObjectiveSpace {
+        ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
+    }
+
+    fn cand(obj: Vec<f64>) -> Candidate<()> {
+        Candidate::new((), Evaluation::new(obj))
+    }
+
+    #[test]
+    fn r2_extremes_are_perfect_at_endpoints() {
+        let s = space_min2();
+        let front = [cand(vec![0.0, 1.0]), cand(vec![1.0, 0.0])];
+        let weights = [vec![1.0, 0.0], vec![0.0, 1.0]];
+        assert!(r2(&front, &weights, &s) < 1e-12);
+    }
+
+    #[test]
+    fn r2_dense_dasdennis_finite_for_uniform_front() {
+        let s = space_min2();
+        let weights = das_dennis(2, 5);
+        let front: Vec<Candidate<()>> = (0..=10)
+            .map(|i| {
+                let t = i as f64 / 10.0;
+                cand(vec![t, 1.0 - t])
+            })
+            .collect();
+        let v = r2(&front, &weights, &s);
+        assert!(v.is_finite());
+        assert!(v >= 0.0);
+    }
+
+    #[test]
+    #[should_panic(expected = "approximation must not be empty")]
+    fn r2_empty_approximation_panics() {
+        let s = space_min2();
+        let weights = vec![vec![1.0, 0.0]];
+        let _: f64 = r2::<()>(&[], &weights, &s);
+    }
+
+    #[test]
+    #[should_panic(expected = "weight set must not be empty")]
+    fn r2_empty_weights_panics() {
+        let s = space_min2();
+        let front = [cand(vec![0.0, 1.0])];
+        let _ = r2(&front, &[], &s);
+    }
+
+    #[test]
+    #[should_panic(expected = "wrong length")]
+    fn r2_wrong_dim_weight_panics() {
+        let s = space_min2();
+        let front = [cand(vec![0.0, 1.0])];
+        let weights = vec![vec![1.0, 0.0, 0.0]];
+        let _ = r2(&front, &weights, &s);
+    }
+}

src/observer/builtin.rs

@@ -0,0 +1,418 @@
+//! Built-in observers covering the common stop conditions.
+
+use std::ops::ControlFlow;
+use std::time::Duration;
+
+use super::{Observer, Snapshot};
+use crate::core::objective::Direction;
+
+/// Halt after a fixed wall-clock duration since `run_with` started.
+///
+/// # Example
+///
+/// ```
+/// use heuropt::prelude::*;
+/// use std::time::Duration;
+///
+/// let stop = MaxTime::new(Duration::from_millis(50));
+/// // pass `&mut stop` to `Optimizer::run_with`.
+/// # let _ = stop;
+/// ```
+#[derive(Debug, Clone, Copy)]
+pub struct MaxTime {
+    pub limit: Duration,
+}
+
+impl MaxTime {
+    pub fn new(limit: Duration) -> Self {
+        Self { limit }
+    }
+}
+
+impl<D> Observer<D> for MaxTime {
+    #[inline]
+    fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
+        if snap.elapsed >= self.limit {
+            ControlFlow::Break(())
+        } else {
+            ControlFlow::Continue(())
+        }
+    }
+}
+
+/// Halt after a target number of generations.
+///
+/// Most algorithms already take a `generations` count in their config,
+/// so this is mostly useful for capping algorithms whose configured
+/// loop is open-ended (or for testing).
+#[derive(Debug, Clone, Copy)]
+pub struct MaxIterations {
+    pub limit: usize,
+}
+
+impl MaxIterations {
+    pub fn new(limit: usize) -> Self {
+        Self { limit }
+    }
+}
+
+impl<D> Observer<D> for MaxIterations {
+    #[inline]
+    fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
+        if snap.iteration >= self.limit {
+            ControlFlow::Break(())
+        } else {
+            ControlFlow::Continue(())
+        }
+    }
+}
+
+/// Halt as soon as the best single-objective fitness reaches `target`.
+///
+/// Direction-aware: for `Minimize` axes the target is reached when
+/// `best ≤ target`; for `Maximize`, when `best ≥ target`.
+///
+/// Multi-objective snapshots (where `Snapshot::best` is `None` or the
+/// problem has more than one objective) are silently ignored — this
+/// observer never breaks them.
+#[derive(Debug, Clone, Copy)]
+pub struct TargetFitness {
+    pub target: f64,
+}
+
+impl TargetFitness {
+    pub fn new(target: f64) -> Self {
+        Self { target }
+    }
+}
+
+impl<D> Observer<D> for TargetFitness {
+    fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
+        if !snap.objectives.is_single_objective() {
+            return ControlFlow::Continue(());
+        }
+        let direction = snap.objectives.objectives[0].direction;
+        if let Some(best) = snap.best
+            && let Some(&v) = best.evaluation.objectives.first()
+        {
+            let hit = match direction {
+                Direction::Minimize => v <= self.target,
+                Direction::Maximize => v >= self.target,
+            };
+            if hit {
+                return ControlFlow::Break(());
+            }
+        }
+        ControlFlow::Continue(())
+    }
+}
+
+/// Halt when the best single-objective fitness has not improved by
+/// more than `tolerance` over the last `window` generations.
+///
+/// Multi-objective snapshots are silently ignored.
+#[derive(Debug, Clone)]
+pub struct Stagnation {
+    pub window: usize,
+    pub tolerance: f64,
+    history: std::collections::VecDeque<f64>,
+}
+
+impl Stagnation {
+    pub fn new(window: usize, tolerance: f64) -> Self {
+        assert!(window > 0, "Stagnation window must be > 0");
+        assert!(
+            tolerance >= 0.0,
+            "Stagnation tolerance must be non-negative"
+        );
+        Self {
+            window,
+            tolerance,
+            history: std::collections::VecDeque::with_capacity(window + 1),
+        }
+    }
+}
+
+impl<D> Observer<D> for Stagnation {
+    fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
+        if !snap.objectives.is_single_objective() {
+            return ControlFlow::Continue(());
+        }
+        let direction = snap.objectives.objectives[0].direction;
+        let v = match snap
+            .best
+            .and_then(|c| c.evaluation.objectives.first().copied())
+        {
+            Some(v) => v,
+            None => return ControlFlow::Continue(()),
+        };
+        // Push to history; cap at window+1 so we always have 1 + window samples.
+        self.history.push_back(v);
+        while self.history.len() > self.window + 1 {
+            self.history.pop_front();
+        }
+        if self.history.len() <= self.window {
+            return ControlFlow::Continue(());
+        }
+        let oldest = self.history.front().copied().unwrap();
+        let newest = self.history.back().copied().unwrap();
+        let improvement = match direction {
+            Direction::Minimize => oldest - newest,
+            Direction::Maximize => newest - oldest,
+        };
+        if improvement <= self.tolerance {
+            ControlFlow::Break(())
+        } else {
+            ControlFlow::Continue(())
+        }
+    }
+}
+
+/// Compose two observers — break if **either** signals a break.
+#[derive(Debug, Clone, Copy)]
+pub struct AnyOf<A, B> {
+    pub a: A,
+    pub b: B,
+}
+
+impl<D, A, B> Observer<D> for AnyOf<A, B>
+where
+    A: Observer<D>,
+    B: Observer<D>,
+{
+    fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
+        // Always poll both so stateful observers (Stagnation) update
+        // their history, then OR the results.
+        let ra = self.a.observe(snap);
+        let rb = self.b.observe(snap);
+        if ra.is_break() || rb.is_break() {
+            ControlFlow::Break(())
+        } else {
+            ControlFlow::Continue(())
+        }
+    }
+}
+
+/// Compose two observers — break only if **both** signal a break in
+/// the same call.
+#[derive(Debug, Clone, Copy)]
+pub struct AllOf<A, B> {
+    pub a: A,
+    pub b: B,
+}
+
+impl<D, A, B> Observer<D> for AllOf<A, B>
+where
+    A: Observer<D>,
+    B: Observer<D>,
+{
+    fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
+        let ra = self.a.observe(snap);
+        let rb = self.b.observe(snap);
+        if ra.is_break() && rb.is_break() {
+            ControlFlow::Break(())
+        } else {
+            ControlFlow::Continue(())
+        }
+    }
+}
+
+/// Call a user closure every `every` generations (default 1 = every
+/// generation). Useful for periodic logging without bloating callback
+/// frequency.
+pub struct Periodic<F> {
+    pub every: usize,
+    counter: usize,
+    pub callback: F,
+}
+
+impl<F> Periodic<F> {
+    pub fn new(every: usize, callback: F) -> Self {
+        assert!(every >= 1, "Periodic every must be >= 1");
+        Self {
+            every,
+            counter: 0,
+            callback,
+        }
+    }
+}
+
+impl<D, F> Observer<D> for Periodic<F>
+where
+    F: FnMut(&Snapshot<'_, D>),
+{
+    fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
+        self.counter += 1;
+        if self.counter >= self.every {
+            self.counter = 0;
+            (self.callback)(snap);
+        }
+        ControlFlow::Continue(())
+    }
+}
+
+/// Tracing-backed observer — emits a structured `debug!` event per
+/// generation with iteration / evaluations / elapsed / best fitness.
+///
+/// Available only with the `tracing` feature.
+#[cfg(feature = "tracing")]
+#[derive(Debug, Default, Clone, Copy)]
+pub struct TracingObserver;
+
+#[cfg(feature = "tracing")]
+impl<D> Observer<D> for TracingObserver {
+    fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
+        let best = snap
+            .best
+            .and_then(|c| c.evaluation.objectives.first().copied());
+        tracing::debug!(
+            iteration = snap.iteration,
+            evaluations = snap.evaluations,
+            elapsed_ms = snap.elapsed.as_millis() as u64,
+            best = ?best,
+            front_size = snap.pareto_front.map(|f| f.len()),
+            "heuropt generation",
+        );
+        ControlFlow::Continue(())
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::core::candidate::Candidate;
+    use crate::core::evaluation::Evaluation;
+    use crate::core::objective::{Objective, ObjectiveSpace};
+
+    fn snap_with_best<'a>(
+        iteration: usize,
+        elapsed_ms: u64,
+        best: Option<&'a Candidate<()>>,
+        objectives: &'a ObjectiveSpace,
+        empty_pop: &'a [Candidate<()>],
+    ) -> Snapshot<'a, ()> {
+        Snapshot {
+            iteration,
+            evaluations: 0,
+            elapsed: Duration::from_millis(elapsed_ms),
+            population: empty_pop,
+            pareto_front: None,
+            best,
+            objectives,
+        }
+    }
+
+    #[test]
+    fn max_time_breaks_after_limit() {
+        let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
+        let pop: Vec<Candidate<()>> = vec![];
+        let mut o = MaxTime::new(Duration::from_millis(100));
+        let s = snap_with_best(0, 50, None, &space, &pop);
+        assert!(o.observe(&s).is_continue());
+        let s = snap_with_best(1, 100, None, &space, &pop);
+        assert!(o.observe(&s).is_break());
+    }
+
+    #[test]
+    fn target_fitness_minimize() {
+        let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
+        let pop: Vec<Candidate<()>> = vec![];
+        let cand = Candidate::new((), Evaluation::new(vec![0.005]));
+        let mut o = TargetFitness::new(0.01);
+        let s = snap_with_best(0, 0, Some(&cand), &space, &pop);
+        assert!(o.observe(&s).is_break());
+    }
+
+    #[test]
+    fn target_fitness_maximize() {
+        let space = ObjectiveSpace::new(vec![Objective::maximize("f")]);
+        let pop: Vec<Candidate<()>> = vec![];
+        let cand_below = Candidate::new((), Evaluation::new(vec![0.5]));
+        let cand_above = Candidate::new((), Evaluation::new(vec![1.5]));
+        let mut o = TargetFitness::new(1.0);
+        let s = snap_with_best(0, 0, Some(&cand_below), &space, &pop);
+        assert!(o.observe(&s).is_continue());
+        let s = snap_with_best(1, 0, Some(&cand_above), &space, &pop);
+        assert!(o.observe(&s).is_break());
+    }
+
+    #[test]
+    fn stagnation_breaks_on_no_improvement() {
+        let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
+        let pop: Vec<Candidate<()>> = vec![];
+        let mut o = Stagnation::new(3, 1e-6);
+
+        // Five generations of "no improvement" — same value every time.
+        for i in 0..3 {
+            let cand = Candidate::new((), Evaluation::new(vec![1.0]));
+            let s = snap_with_best(i, 0, Some(&cand), &space, &pop);
+            // First `window` calls just fill history; should not break.
+            assert!(o.observe(&s).is_continue());
+        }
+        let cand = Candidate::new((), Evaluation::new(vec![1.0]));
+        let s = snap_with_best(3, 0, Some(&cand), &space, &pop);
+        assert!(o.observe(&s).is_break());
+    }
+
+    #[test]
+    fn stagnation_does_not_break_on_improvement() {
+        let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
+        let pop: Vec<Candidate<()>> = vec![];
+        let mut o = Stagnation::new(2, 1e-6);
+        let values = [1.0, 0.9, 0.8, 0.7];
+        for (i, &v) in values.iter().enumerate() {
+            let cand = Candidate::new((), Evaluation::new(vec![v]));
+            let s = snap_with_best(i, 0, Some(&cand), &space, &pop);
+            assert!(o.observe(&s).is_continue(), "iter {i}");
+        }
+    }
+
+    #[test]
+    fn anyof_breaks_when_either_breaks() {
+        let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
+        let pop: Vec<Candidate<()>> = vec![];
+        let cand = Candidate::new((), Evaluation::new(vec![5.0]));
+        let mut o =
+            <MaxIterations as Observer<()>>::or(MaxIterations::new(3), TargetFitness::new(1.0));
+        for i in 0..3 {
+            let s = snap_with_best(i, 0, Some(&cand), &space, &pop);
+            assert!(o.observe(&s).is_continue(), "iter {i}");
+        }
+        // iteration = 3 hits MaxIterations limit → break
+        let s = snap_with_best(3, 0, Some(&cand), &space, &pop);
+        assert!(o.observe(&s).is_break());
+    }
+
+    #[test]
+    fn periodic_calls_callback_every_n() {
+        let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
+        let pop: Vec<Candidate<()>> = vec![];
+        let mut count = 0_usize;
+        {
+            let mut o = Periodic::new(3, |_: &Snapshot<'_, ()>| count += 1);
+            for i in 0..10 {
+                let s = snap_with_best(i, 0, None, &space, &pop);
+                let _ = o.observe(&s);
+            }
+        }
+        assert_eq!(count, 3); // every 3rd of 10 = generations 2, 5, 8
+    }
+
+    #[test]
+    fn closure_implements_observer() {
+        let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
+        let pop: Vec<Candidate<()>> = vec![];
+        let mut count = 0_usize;
+        let mut closure = |_: &Snapshot<'_, ()>| -> ControlFlow<()> {
+            count += 1;
+            if count >= 2 {
+                ControlFlow::Break(())
+            } else {
+                ControlFlow::Continue(())
+            }
+        };
+        let s = snap_with_best(0, 0, None, &space, &pop);
+        assert!(<_ as Observer<()>>::observe(&mut closure, &s).is_continue());
+        assert!(<_ as Observer<()>>::observe(&mut closure, &s).is_break());
+    }
+}

src/observer/mod.rs

@@ -0,0 +1,101 @@
+//! Per-generation observation, callbacks, and stop conditions.
+//!
+//! Algorithms accept an [`Observer`] via [`Optimizer::run_with`] and call
+//! it once per generation (where "generation" makes sense for that
+//! algorithm — see each algorithm's docs). Returning
+//! [`std::ops::ControlFlow::Break`] from an observer halts the optimizer
+//! and the partial [`OptimizationResult`] is returned to the caller.
+//!
+//! Observers can be composed with [`builtin::AnyOf`] / [`builtin::AllOf`].
+//!
+//! [`OptimizationResult`]: crate::core::result::OptimizationResult
+//! [`Optimizer::run_with`]: crate::traits::Optimizer::run_with
+
+pub mod builtin;
+mod snapshot;
+
+pub use snapshot::Snapshot;
+
+use std::ops::ControlFlow;
+
+use crate::core::candidate::Candidate;
+use crate::core::objective::ObjectiveSpace;
+
+/// A callback invoked by an [`Optimizer`](crate::traits::Optimizer)
+/// after every generation. Return [`ControlFlow::Break`] to halt
+/// the optimizer; [`ControlFlow::Continue`] to keep going.
+///
+/// Implement directly for stateful observers that need to track
+/// history (e.g. stagnation detection, convergence trace logging).
+/// For simple stop conditions, use the helpers in
+/// [`builtin`](crate::observer::builtin).
+pub trait Observer<D> {
+    /// Inspect the latest snapshot. Return [`ControlFlow::Break`] to
+    /// halt the run; [`ControlFlow::Continue`] to keep going.
+    fn observe(&mut self, snapshot: &Snapshot<'_, D>) -> ControlFlow<()>;
+
+    /// Compose with another observer that fires when *either* of them
+    /// signals a break.
+    fn or<O: Observer<D>>(self, other: O) -> builtin::AnyOf<Self, O>
+    where
+        Self: Sized,
+    {
+        builtin::AnyOf { a: self, b: other }
+    }
+
+    /// Compose with another observer that fires when *both* of them
+    /// signal a break in the same call.
+    fn and<O: Observer<D>>(self, other: O) -> builtin::AllOf<Self, O>
+    where
+        Self: Sized,
+    {
+        builtin::AllOf { a: self, b: other }
+    }
+}
+
+/// `()` is the no-op observer. Used as the default when callers don't
+/// want any callbacks (it's what `run` uses internally).
+impl<D> Observer<D> for () {
+    #[inline]
+    fn observe(&mut self, _: &Snapshot<'_, D>) -> ControlFlow<()> {
+        ControlFlow::Continue(())
+    }
+}
+
+/// Closures of the right shape implement Observer too — short-form
+/// for one-liner callbacks.
+impl<D, F> Observer<D> for F
+where
+    F: FnMut(&Snapshot<'_, D>) -> ControlFlow<()>,
+{
+    #[inline]
+    fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
+        self(snap)
+    }
+}
+
+/// Build a snapshot for the "final notification" path of the default
+/// `run_with` impl on [`Optimizer`](crate::traits::Optimizer).
+///
+/// Algorithm impls that override `run_with` to call the observer per
+/// generation should construct their own snapshots inline rather than
+/// using this helper, because they have richer per-generation state.
+pub fn finalize_snapshot<'a, D>(
+    iteration: usize,
+    evaluations: usize,
+    elapsed: std::time::Duration,
+    population: &'a [Candidate<D>],
+    pareto_front: Option<&'a [Candidate<D>]>,
+    best: Option<&'a Candidate<D>>,
+    objectives: &'a ObjectiveSpace,
+) -> Snapshot<'a, D> {
+    Snapshot {
+        iteration,
+        evaluations,
+        elapsed,
+        population,
+        pareto_front,
+        best,
+        objectives,
+    }
+}

src/observer/snapshot.rs

@@ -0,0 +1,45 @@
+//! Per-generation observation payload passed to [`Observer`](super::Observer).
+
+use std::time::Duration;
+
+use crate::core::candidate::Candidate;
+use crate::core::objective::ObjectiveSpace;
+
+/// A view of an optimizer's state at one generation boundary.
+///
+/// Borrowed (`&'a ...`) rather than owned so the algorithm doesn't
+/// have to clone the whole population on every call. Observers that
+/// need to retain values across calls should clone what they need
+/// out of the snapshot.
+#[derive(Debug)]
+pub struct Snapshot<'a, D> {
+    /// Zero-indexed generation count. The first call is `iteration = 0`
+    /// for "after the initial population was built and evaluated";
+    /// subsequent calls are after generation 1, 2, …
+    pub iteration: usize,
+
+    /// Total `Problem::evaluate` calls so far, including the initial
+    /// population.
+    pub evaluations: usize,
+
+    /// Wall-clock time since `run_with` started.
+    pub elapsed: Duration,
+
+    /// The current population (whatever the algorithm considers the
+    /// "live" set this generation). For steady-state algorithms this
+    /// is the post-replacement population.
+    pub population: &'a [Candidate<D>],
+
+    /// The current Pareto front, if the algorithm tracks one. `None`
+    /// for single-objective algorithms.
+    pub pareto_front: Option<&'a [Candidate<D>]>,
+
+    /// The current best candidate. `Some` for single-objective
+    /// algorithms; `None` for multi-objective unless the algorithm
+    /// tracks a notion of best (some don't).
+    pub best: Option<&'a Candidate<D>>,
+
+    /// The objective space, useful for observers that need to convert
+    /// raw objective values to minimization-oriented form.
+    pub objectives: &'a ObjectiveSpace,
+}

src/prelude.rs

@@ -4,6 +4,8 @@
 //! use heuropt::prelude::*;
 //! ```
 
+#[cfg(feature = "async")]
+pub use crate::core::async_problem::AsyncProblem;
 pub use crate::core::{
     Candidate, Direction, Evaluation, Objective, ObjectiveSpace, OptimizationResult,
     PartialProblem, Population, Problem, Rng, rng_from_seed,
@@ -11,6 +13,14 @@ pub use crate::core::{
 
 pub use crate::traits::{Initializer, Optimizer, Repair, Variation};
 
+#[cfg(feature = "tracing")]
+pub use crate::observer::builtin::TracingObserver;
+pub use crate::observer::{
+    Observer, Snapshot,
+    builtin::{AllOf, AnyOf, MaxIterations, MaxTime, Periodic, Stagnation, TargetFitness},
+};
+pub use std::ops::ControlFlow;
+
 pub use crate::pareto::{
     Dominance, ParetoArchive, best_candidate, crowding_distance, das_dennis, non_dominated_sort,
     pareto_compare, pareto_front,

src/traits/optimizer.rs

@@ -1,18 +1,71 @@
 //! The single trait users implement to add a new optimizer.
 
+use std::time::{Duration, Instant};
+
 use crate::core::problem::Problem;
 use crate::core::result::OptimizationResult;
+use crate::observer::{Observer, Snapshot};
 
 /// An optimizer that runs to completion in a single call.
 ///
 /// Implementations own their main loop, manage their own state, and return an
-/// [`OptimizationResult`]. v1 deliberately does not expose a step-by-step API
+/// [`OptimizationResult`]. Invalid configuration panics with a clear
-/// or an associated error type — invalid configuration may panic with a clear
+/// message rather than returning a `Result`.
-/// message.
 pub trait Optimizer<P>
 where
     P: Problem,
 {
     /// Run the optimizer to completion against `problem`.
     fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision>;
+
+    /// Run with an [`Observer`] called after each generation.
+    ///
+    /// The observer can halt the run by returning
+    /// [`std::ops::ControlFlow::Break`]; the partial result is still
+    /// returned. Built-in observers in
+    /// [`heuropt::observer::builtin`](crate::observer::builtin) cover
+    /// the common stop conditions (`MaxTime`, `TargetFitness`,
+    /// `Stagnation`, …).
+    ///
+    /// **Default impl:** falls back to `run` plus a single final
+    /// notification. Algorithms that override this method get true
+    /// per-generation observation; algorithms that don't get a single
+    /// notification at the end. The trait-level docstring on each
+    /// algorithm calls out which behavior it supports.
+    fn run_with<O>(&mut self, problem: &P, observer: &mut O) -> OptimizationResult<P::Decision>
+    where
+        O: Observer<P::Decision>,
+    {
+        let started = Instant::now();
+        let result = self.run(problem);
+        let elapsed = started.elapsed();
+        notify_final(&result, elapsed, problem, observer);
+        result
+    }
+}
+
+/// Helper used by the default `run_with` impl: build a single final-
+/// state snapshot and hand it to the observer once. Algorithms that
+/// override `run_with` for per-generation reporting don't go through
+/// this path — they construct their own per-iteration snapshots.
+fn notify_final<P, O>(
+    result: &OptimizationResult<P::Decision>,
+    elapsed: Duration,
+    problem: &P,
+    observer: &mut O,
+) where
+    P: Problem,
+    O: Observer<P::Decision>,
+{
+    let objectives = problem.objectives();
+    let snap = Snapshot {
+        iteration: result.generations,
+        evaluations: result.evaluations,
+        elapsed,
+        population: result.population.as_slice(),
+        pareto_front: Some(result.pareto_front.as_slice()),
+        best: result.best.as_ref(),
+        objectives: &objectives,
+    };
+    let _ = observer.observe(&snap);
 }