DEAP
Evolutionary algorithm components for population-based optimization experiments.
External resource. No execution or independent verification is claimed here.
How do mutation probabilities affect success frequency under a fixed evolutionary-search budget?
Enumerate the finite objective landscape independently, fix initialization and evaluation accounting, and report all predefined seeds and unsuccessful runs.
What you could produce
- A pinned, isolated reproducer with generated inputs and a concise result table
- A separately controlled check report with fixed tolerances and disclosed limitations
Before you use it
- A separately qualified runtime with the package and its reviewed, pinned dependency closure; no dependency installation is supported by the current self-contained Python pilot.
Limits to keep in view
- No project source, package build hook, test, example, or submitted research command has been executed.
- The documented pilot supports self-contained Python 3.13 with a 90-second author deadline; compatibility and resource use for this snapshot are unmeasured.
Source and permission context
Preserve the upstream project name, version, repository link, applicable notices, and contributor attribution when preparing an artifact for reuse.
Catalog listing reviewed. This review covers the description and source links displayed here.
Approved for catalog metadata and links. The displayed entry identifies DEAP with the concise collection-authored summary “Evolutionary algorithm components for population-based optimization experiments.” and points to the public upstream repository https://github.com/DEAP/deap. The wording describes function and possible investigation without reproducing upstream source or documentation, claiming execution, or implying endorsement or rights beyond the recorded scopes.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
code default scope · LGPL-3.0-or-later
The pinned package initializer's licensing header explicitly grants LGPL version 3 or later; LICENSE.txt supplies the corresponding LGPL text. License text: LICENSE.txt at commit 8a96fd3a75026f7b30e835f595a5199c75634ddf; no repository-wide clearance is claimed.
Inspect the license evidence ↗Before copying source material
- Bundled datasets, examples, submodules, vendored code, and dependency licenses have not been audited; the observed top-level license does not clear all of them.
- README and documentation rights were not independently resolved from the main code license; this catalog only links and describes.