Add an example of BIPOP-CMA-ES

.github/workflows/examples.yml

@@ -27,4 +27,5 @@ jobs:
           pip install --progress-bar off -U .
       - run: python examples/quadratic_function.py
       - run: python examples/ipop_cmaes.py
+      - run: python examples/bipop_cmaes.py
       - run: python examples/optuna_sampler.py

examples/bipop_cmaes.py

@@ -0,0 +1,76 @@
+import math
+import numpy as np
+from cmaes import CMA
+
+
+def ackley(x1, x2):
+    return (
+        -20 * math.exp(-0.2 * math.sqrt(0.5 * (x1 ** 2 + x2 ** 2)))
+        - math.exp(0.5 * (math.cos(2 * math.pi * x1) + math.cos(2 * math.pi * x2)))
+        + math.e
+        + 20
+    )
+
+
+def main():
+    seed = 0
+    rng = np.random.RandomState(0)
+
+    bounds = np.array([[-32.768, 32.768], [-32.768, 32.768]])
+    lower_bounds, upper_bounds = bounds[:, 0], bounds[:, 1]
+
+    mean = lower_bounds + (rng.rand(2) * (upper_bounds - lower_bounds))
+    sigma = 32.768 * 2 / 5  # 1/5 of the domain width
+    optimizer = CMA(mean=mean, sigma=sigma, bounds=bounds, seed=0)
+
+    n_restarts = 0  # A small restart doesn't count in the n_restarts
+    small_n_eval, large_n_eval = 0, 0
+    popsize0 = optimizer.population_size
+    inc_popsize = 2
+
+    # Initial run is with "normal" population size; it is
+    # the large population before first doubling, but its
+    # budget accounting is the same as in case of small
+    # population.
+    poptype = "small"
+
+    while n_restarts <= 5:
+        solutions = []
+        for _ in range(optimizer.population_size):
+            x = optimizer.ask()
+            value = ackley(x[0], x[1])
+            solutions.append((x, value))
+            # print("{:10.5f}  {:6.2f}  {:6.2f}".format(value, x[0], x[1]))
+        optimizer.tell(solutions)
+
+        if optimizer.should_stop():
+            seed += 1
+            n_eval = optimizer.population_size * optimizer.generation
+            if poptype == "small":
+                small_n_eval += n_eval
+            else:  # poptype == "large"
+                large_n_eval += n_eval
+
+            if small_n_eval < large_n_eval:
+                poptype = "small"
+                popsize_multiplier = inc_popsize ** n_restarts
+                popsize = math.floor(
+                    popsize0 * popsize_multiplier ** (rng.uniform() ** 2)
+                )
+            else:
+                poptype = "large"
+                n_restarts += 1
+                popsize = popsize0 * (inc_popsize ** n_restarts)
+            mean = lower_bounds + (rng.rand(2) * (upper_bounds - lower_bounds))
+            optimizer = CMA(
+                mean=mean,
+                sigma=sigma,
+                bounds=bounds,
+                seed=seed,
+                population_size=popsize,
+            )
+            print("Restart CMA-ES with popsize={} ({})".format(popsize, poptype))
+
+
+if __name__ == "__main__":
+    main()