Random Search for Hyperparameter Optimization
Algorithms AIMech:RandomSearchHyperparameterOptimization DRAFT
Aliases: Random Hyperparameter Search
A hyperparameter optimization procedure that draws trial configurations independently from a specified search distribution, evaluates their validation performance, and selects a favorable evaluated configuration without adapting subsequent draws to observed trial scores.
Identity Notes & Concept Scope
This identity is random sampling for model hyperparameter selection, not stochastic gradient descent, random graph traversal, a quasi-random low-discrepancy design, or every randomized optimizer. Independence concerns configuration draws, not statistical independence of scores measured on shared validation data. Unlike TPE and BOHB, past scores do not fit a proposal model; unlike PBT, the search does not copy trained weights between population members. No normalized identity match was found across all 442 pinned AIO labels/scoped synonyms and 256 preceding native IDs/names/aliases. Full-checkout fixed-string searches included ignored and hidden files in both worktree and main checkout, returning 2 lines in each checkout. These were prior random-search candidate leads, not existing records for these identities. Nine exact AIO CLASS nodes with synonyms and outgoing edges and six neighboring native mechanisms were inspected. Absence is bounded to this snapshot and semantic neighborhood.
Claims & Grounded Evidence
1 Claim
Mechanism
#method
Specify the valid hyperparameter space and its sampling distribution, independently sample a finite set of configurations, train/evaluate each under the chosen protocol, and select by validation loss. The paper initially describes uniform sampling in search coordinates, while its concrete neural-network and DBN spaces include categorical choices, log-domain draws and conditional architecture settings. Therefore uniformity in raw numerical values and independent activation of every coordinate are not defining requirements. More trials extend the same search distribution without requiring a Cartesian grid. The sampling rule does not itself prescribe equal runtime or forbid per-trial early stopping; the neural-network case study includes a validation-based stopping rule. Test performance is evaluated separately from the validation criterion used to choose a configuration.
⚠️ Curated Limitations & Negative Results
- Coverage depends on the chosen distribution, parameterization and budget; a region assigned zero probability cannot be discovered by these draws. Low effective dimensionality explains a possible advantage over grids, not a universal performance guarantee.
- An observed validation winner need not be the best test or population-risk model. The cited experiments and uncertainty-reporting procedure are context, not reproduced performance results.
- The record retains the nonadaptive configuration-sampling procedure; score-adaptive proposals, population inheritance and cross-trial resource scheduling are additional mechanisms.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:TrainingStrategy
TrainingStrategy
|
related_match | This procedure organizes hyperparameter selection or allocation of model-training resources. The pinned AIO target describes training strategies as preprocessing; the relation connects these scopes without asserting that this search algorithm is preprocessing, an identical concept, or a subclass. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
James Bergstra, Yoshua Bengio
|
primary paper
full_text
|
sha256:01e88a175a36…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T11:28:05Z
Updated: 2026-10-11T11:28:05Z