Grid Search for Hyperparameter Optimization
Algorithms AIMech:GridSearchHyperparameterOptimization DRAFT
Aliases: Cartesian Hyperparameter Grid Search
A hyperparameter optimization procedure that evaluates a finite Cartesian product of chosen values for each configuration variable and selects a configuration using its validation performance.
Identity Notes & Concept Scope
This is the general Cartesian-grid hyperparameter search, distinct from the maintained fairness reduction that enumerates Lagrange-multiplier-derived costs and constructs cost-sensitive classifier responses. It is also distinct from grid-based spatial path planning, independent random search, adaptive refinement of a grid, and halving-based resource allocation. Grid locations can be chosen in transformed coordinates; equal spacing in raw numerical values is not required by the Cartesian-product definition. No normalized identity match was found across all 442 pinned AIO labels/scoped synonyms and 259 preceding native IDs/names/aliases. Full-checkout fixed-string searches included ignored and hidden files in both worktree and main checkout, returning 26 lines in each checkout. These were prior candidate leads, source references and neighboring-method mentions, 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
Choose a finite value set L_k for each of K hyperparameters. Form candidate configurations from the Cartesian product of these sets, giving product_k |L_k| combinations. Train/evaluate the candidates under the chosen validation protocol and select the configuration with favorable validation loss. The finite set of configuration locations is specified without fitting a proposal model from earlier scores. The paper’s Figure 1 shows why varying irrelevant coordinates can repeatedly evaluate the same values along a relevant coordinate. That coverage property does not prescribe the optimizer used inside each training run, a worker schedule, or a particular cross-validation fold count.
⚠️ Curated Limitations & Negative Results
- Exhaustiveness applies to the selected finite product, not to continuous values between grid points or excluded settings. The number of combinations grows multiplicatively with coordinate-set sizes.
- Validation selection does not guarantee best test or population performance. Relative efficiency versus random search depends on dimension, parameterization, important coordinates, ranges and budget.
- Conditional or incompatible parameter settings require an explicitly valid search space. Adaptive grid refinement and outcome-dependent resource allocation are additional procedures, not defining steps of this fixed-grid concept.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:TrainingStrategy
TrainingStrategy
|
related_match | The hyperparameter-optimization use of this procedure selects training settings or allocates resources to candidate models. This relates it to AIO’s training-strategy concept without making the algorithm a preprocessing subclass or equating broader best-candidate selection with a machine-learning task. |
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: 2026-10-11T11:42:59Z
Updated: 2026-10-11T11:42:59Z