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Genetic algorithm

Algorithms AIMech:GeneticAlgorithm DRAFT

Aliases: GA

Evolutionary optimization using fitness-based selection and genetic variation of candidate solutions.

Identity Notes & Concept Scope MachineLearningTask does not specify evolutionary fitness selection, recombination and mutation; genetic optimization is not necessarily learning from data.

Claims & Grounded Evidence

1 Claim

Mechanism #mechanism

Fitness evaluation and selection are combined with crossover and mutation to produce successive populations.

supports Source: DEAP algorithms: eaSimple · Locator: DEAP algorithms documentation, eaSimple pseudocode: evaluate, select, varAnd, mate and mutate

⚠️ Curated Limitations & Negative Results

This record supports method identity and the stated mechanism only; no universal performance or biological efficacy claim is curated.
Proposed placement is an inference from the existing AIO hierarchy; maintainers must resolve task, algorithm and model scope before acceptance. These are procedures, not trained model instances; current AIO branch definitions mix abstraction levels.

Artificial Intelligence Ontology (AIO) Grounding

Mapped AIO Classes

AIO Target Class	Mapping Relation	Mapping Rationale
`AIO:MachineLearningTask` Machine Learning Task	related_match	MachineLearningTask does not specify evolutionary fitness selection, recombination and mutation; genetic optimization is not necessarily learning from data. Mapping is local curation, not an upstream subclass assertion.

Evidence Sources & Primary Literature

1 Source

Source ID	Publication / Document	Type / Access	Reference & Checksum
`method-source`	DEAP algorithms: eaSimple DEAP contributors	official documentation documentation

Curation Provenance

Curator: Codex

Method: ai_assisted

Model: GPT-6

Created: 2026-10-11T03:01:34Z

Updated: 2026-10-11T03:01:34Z

Source File: data/curated/GeneticAlgorithm.yaml