BasicILS
Algorithms AIMech:BasicILS DRAFT
Aliases: BasicILS(N)
A ParamILS algorithm-configuration method that compares candidate configurations using cost estimates on the same fixed number of target-algorithm runs, paired by problem instance and random seed.
Identity Notes & Concept Scope
BasicILS(N) specializes the ParamILS framework by fixing the comparison sample size N. N counts instance/seed evaluations, not necessarily distinct instances. It differs from FocusedILS, which grows evaluation prefixes adaptively, and from random search, which does not use ParamILS neighborhood perturbation and local search. No normalized identity match was found across all 442 pinned AIO labels/scoped synonyms and 265 preceding native IDs/names/aliases. Full-checkout fixed-string searches included ignored and hidden files in both worktree and main checkout, returning 20 lines in the worktree and 25 in main. These were prior candidate leads, source references, neighboring-method mentions and five unrelated ROAR namespace entries in ignored dependencies, not existing records for these identities. Seven 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
Use ParamILS initialization, randomized one-exchange local search, perturbation, nonworsening acceptance and random restart. Implement each comparison by obtaining the chosen cost statistic for both configurations on the first N pairs in the shared instance/seed sequence, reusing cached compatible target-algorithm runs. The uncapped comparison returns true when the first configuration’s estimate is no greater than the second’s. Track the best encountered configuration under that empirical objective. The fixed comparison budget is independent of the overall time or iteration budget used to stop the configurator.
⚠️ Curated Limitations & Negative Results
- The 2009 formulation has a finite configuration domain; numerical parameters require discretization. Objective, run cutoff and training-instance distribution affect the selected configuration. Independent held-out evaluation is needed to assess generalization; no universal solver speedup is asserted.
- The printed first-improvement loop accepts the supplied non-strict comparison. Equal-cost neighboring configurations can therefore cycle under a literal reading; this record does not assert unconditional termination of that loop or executable equivalence to a software release.
- Adaptive capping is a separable acceleration, not the defining identity of this search/comparison method. Section4 describes trajectory-preserving and more aggressive alternatives. Procedure4 prints a max operation inconsistent with shortening the run cutoff described in the prose, and uses inconsistent cutoff symbols; the printed routine is not treated as verified executable code.
- A small fixed N can give unreliable rankings and overfitting; a large N increases evaluation cost and reduces search progress at a fixed time budget. These are experimental-design tradeoffs, not a guarantee that one N is optimal.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No inspected AIO target captures this general algorithm-configuration procedure or a defining component. The local algorithm remains supported by its primary source; this is a bounded mapping assessment, not absence from all ontologies.
Search Scope: All 442 pinned AIO CLASS identities and scoped synonyms, all 265 preceding maintained identities, ignored/hidden checkout text, and semantic inspection of seven AIO targets plus neighboring configuration/resource-allocation methods.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:TrainingStrategy
|
The pinned definition concerns preprocessing/training of machine-learning models. This method configures target algorithms generally; an optional use for model tuning does not establish a training-strategy identity or necessary relation. |
AIO:MachineLearningTask
|
The target is a broad process for building methods that learn from data. Configuring an arbitrary solver over instances need not build such a learning method; shared use of observations does not justify equivalence or subclassing. |
AIO:MetaLearning
|
The pinned task learns from metadata about machine-learning experiments. These methods require no collection of ML experiments or learned cross-task prior; solver configuration alone is not that identity. |
AIO:ActiveLearning
|
The pinned task queries labels for data points. Choosing configuration evaluations or instance runs is not label acquisition in that defined sense. |
AIO:ReinforcementLearning
|
Exploration and empirical feedback do not by themselves establish the pinned learning-task identity; no reward-learning agent or policy-learning component is required by these configurators. |
AIO:RandomForest
|
These methods contain no defining decision-tree ensemble surrogate; random sampling, randomized local search and rank tests are not random forests. |
AIO:EvaluationBias
|
This is a bias concept, not a configuration algorithm. Potential overfitting or distribution mismatch is a limitation rather than an identity mapping. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle
|
primary paper
full_text
|
ID: doi:10.1613/jair.2861
sha256:3a1223d28e13…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T12:04:49Z
Updated: 2026-10-11T12:04:49Z
Source File:
data/curated/BasicILS.yaml