egobox-egor-tuning

name	egobox-egor-tuning
description	Use this skill whenever the user is working with the Egor optimizer. Triggers on any mention of: Egor optimizer Bayesian optimization in Rust/Python or requests to minimize expensive black-box functions using surrogate models.

EGOR Tuning Skill

Goal

Automatically select and adapt EGOR optimization parameters based on:

• problem dimension
• objective evaluation runtime
• available optimization budget
• parallel evaluation availability
• convergence progress

Workflow

1. Estimate problem characteristics.
2. Select initial EGOR strategy.
3. Monitor optimization progress.
4. Adapt exploration/exploitation.

Basic rules

• More dimensions -> reduce direct sampling, use surrogate strategies.
• Expensive evaluations -> maximize information gain.
• Poor progress -> increase exploration.

Advanced strategies

Poor convergence

If optimization stagnates:

• enable TREGO
• switch kernel from SquaredExponential to Matern52
• increase exploration

Dimension > 10

Use KPLS for Gaussian process fitting.

Examples:

• dimension 20: kpls_dim=5
• dimension 100: kpls_dim=10

Dimension > 50

Use CoEGO.

Example:

• dimension 100: n_coop_comp=5

Parallel evaluations

If evaluations can run in parallel: use qEIConfig.

Examples:

• dimension 50: batch=5
• dimension 100: batch=10