feat(algorithms): add RandomSearch baseline optimizer

The reference baseline and the spec's recommended starting example. Per
iteration it asks the initializer for `batch_size` decisions, evaluates
each, and accumulates them. At the end it returns the full population
plus the Pareto front and (if single-objective) the best feasible
candidate. `generations` equals `iterations`; `evaluations` equals
`iterations * batch_size` (spec §12.1).

Includes a tiny single-objective sphere test problem under
`tests_support` that later algorithm tests will reuse.

src/tests_support/mod.rs

@@ -0,0 +1,40 @@
+//! Shared, deliberately tiny test problems used by algorithm unit tests.
+//!
+//! Not part of the public API.
+
+use crate::core::evaluation::Evaluation;
+use crate::core::objective::{Objective, ObjectiveSpace};
+use crate::core::problem::Problem;
+
+/// 1-D minimization sphere `f(x) = x^2`. Single objective, always feasible.
+pub struct Sphere1D;
+
+impl Problem for Sphere1D {
+    type Decision = Vec<f64>;
+
+    fn objectives(&self) -> ObjectiveSpace {
+        ObjectiveSpace::new(vec![Objective::minimize("f")])
+    }
+
+    fn evaluate(&self, decision: &Vec<f64>) -> Evaluation {
+        Evaluation::new(vec![decision[0] * decision[0]])
+    }
+}
+
+/// Schaffer N.1 — the textbook two-objective minimization warm-up:
+///
+/// `f1(x) = x^2`, `f2(x) = (x - 2)^2`. Always feasible.
+pub struct SchafferN1;
+
+impl Problem for SchafferN1 {
+    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 v = x[0];
+        Evaluation::new(vec![v * v, (v - 2.0).powi(2)])
+    }
+}