Skip to content

differential_evolution

Evolution strategy for continuous optimization. Maintains a population, mutates by adding weighted differences between individuals.

Example

from solvor import differential_evolution

def objective(x):
    return sum(xi**2 for xi in x)

result = differential_evolution(
    objective,
    bounds=[(−10, 10)] * 5,
    pop_size=50,
    max_iter=1000
)
print(result.solution)  # Close to [0, 0, 0, 0, 0]

Parameters

Parameter Description
bounds List of (min, max) for each dimension
pop_size Population size (default: 10 × dimensions)
F Mutation scale (default: 0.8)
CR Crossover probability (default: 0.9)

When to Use

  • Global search in continuous spaces
  • Non-convex landscapes with many local minima
  • No gradient information

See Also