differential_evolution
A reference for population-based stochastic optimization with bounds, constraints, initialization, updating and random-state controls.
External resource. No execution or independent verification is claimed here.
At equal objective-evaluation budgets, how often are known target values reached across independent initial populations?
An evaluator supplies bounded objectives with known optima, counts calls independently and recomputes objective and constraint values at every reported candidate.
What you could produce
- A versioned minimal protocol, evaluator-owned test cases, and a comparison report with numerical/statistical uncertainty and failures.
Before you use it
- SciPy 1.18.0
- Compatible NumPy and native numerical-library build; exact environment not prepared
Limits to keep in view
- No upstream example, source package, build hook or submitted code was executed.
- The current qualified pilot accepts only self-contained Python 3.13 with a 90-second deadline. This reference's package/runtime is not qualified for that path.
- The proposed protocol requires bounded resource estimates and an independently controlled evaluator before any scientific execution claim.
Source and permission context
SciPy documentation. differential_evolution. https://docs.scipy.org/doc/scipy-1.18.0/reference/generated/scipy.optimize.differential_evolution.html; observed version 1.18.0.
Rights need review. Review the scope and upstream conditions before reuse.
documentation · BSD-3-Clause
SciPy's versioned developer policy explicitly includes documentation under its default BSD license, subject to separately specified exceptions; the matching 1.18.0 LICENSE.txt identifies the three-clause terms.
Inspect the license evidence ↗Still unresolved
- Third-party figures, linked papers, datasets and dependency licenses have not been assessed; no external content is copied into this catalog.