{"adoption": {"forks": 500, "observed_at": "2026-08-28T04:06:27.547715+00:00", "stars": 2220}, "canonical_url": "https://ross.abutalabs.com/products/geneticalgorithmpython", "card": {"archived": false, "artifact_type": "library", "description": "Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).", "domain": ["machine-learning", "artificial-intelligence", "deep-learning"], "enriched": true, "function": ["machine-learning", "deep-learning"], "health_score": 94, "homepage": "https://pygad.readthedocs.io", "language": "Python", "license": "BSD-3-Clause", "license_family": "permissive", "maturity": "active", "member_repos": ["ahmedfgad/GeneticAlgorithmPython"], "name": "ahmedfgad/GeneticAlgorithmPython", "platform": ["python"], "pushed_at": "2026-07-09T04:06:44+00:00", "repo": "ahmedfgad/GeneticAlgorithmPython", "stars": 2220, "tags": ["genetic-algorithm", "evolutionary-algorithms", "optimization", "numpy", "keras", "pytorch", "metaheuristics", "multi-objective-optimization"], "topics": ["python", "genetic-algorithm", "optimization", "numpy", "pygad", "pygad-documentation", "neural-networks", "machine-learning", "deep-learning", "evolutionary-algorithms"], "urls": [], "use_cases": ["solve optimization problems with a genetic algorithm in python", "train keras neural networks using a genetic algorithm", "optimize pytorch model weights with evolutionary search", "run multi-objective optimization in python", "find optimal parameters with a metaheuristic instead of gradient descent", "feature selection using genetic algorithms"], "what_it_is": "PyGAD is an open-source Python 3 library for implementing the genetic algorithm to optimize single- and multi-objective problems. It can also train machine learning models, with built-in support for Keras and PyTorch neural networks.", "when_to_avoid": ["you need industrial-strength large-scale evolutionary computation with massive parallelism", "your problem is smooth and differentiable, where standard gradient-based optimizers are faster", "you need other metaheuristics like particle swarm or simulated annealing out of the box"], "when_to_choose": ["you need a simple, well-documented genetic algorithm library in Python", "you want to train or fine-tune Keras/PyTorch models without gradients", "your objective function is non-differentiable or black-box", "you need both single- and multi-objective evolutionary optimization"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/geneticalgorithmpython", "repo": "ahmedfgad/GeneticAlgorithmPython", "role": "main", "score": 80}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:06:27.547715+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T02:45:49.936979+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "d1abe20af2ddc9ac6d62582600385c348e148dc9ac10c9f514fd1094c13c7bc9", "fetched_at": "2026-08-28T04:06:27.547715+00:00", "kind": "readme", "missing": false, "url": "https://github.com/ahmedfgad/GeneticAlgorithmPython"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 91, "longevity": 100, "rhythm": 55}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": [], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2822, "days_push": 55, "days_rel": 89, "gap_med": 182, "n_releases_24m": 4}, "score": 80, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}