---
title: "Benchmark Examples"
---

# Benchmark Examples

These tutorials demonstrate how to recreate results from various papers and common studies using BoFire's benchmarking capabilities. Benchmarks are useful for validating optimization strategies and comparing performance across different approaches.

## Available Benchmarks

### [001 - Himmelblau Function](001-Himmelblau.qmd)
Classic optimization benchmark using the Himmelblau function.

### [002 - DTLZ2](002-DTLZ2.qmd)
Multi-objective optimization using the DTLZ2 test problem.

### [003 - Hartmann with N-Choose-K](003-Hartmann_with_nchoosek.qmd)
Hartmann function optimization with n-choose-k constraints.

### [004 - 30-Dimensional Branin](004-30dimBranin.qmd)
High-dimensional optimization using an extended Branin function.

### [005 - Himmelblau with Outliers](005-Himmelblau_with_outliers.qmd)
Robust optimization in the presence of outliers.

### [006 - Bayesian Optimization over Molecules](006-Bayesian_optimization_over_molecules.qmd)
Molecular optimization using chemical fingerprints and specialized kernels.

### [007 - LSRBO](007-LSRBO.qmd)
Local search-based Bayesian optimization.

### [008 - ZDT1](008-ZDT1.qmd)
Multi-objective optimization using the ZDT1 test problem.

### [009 - BNH](009-BNH.qmd)
Binh and Korn multi-objective test function.

### [010 - TNK](010-TNK.qmd)
Tanaka multi-objective test function.

### [011 - Active Learning](011-ActiveLearning.qmd)
Active learning strategies for experimental design.

### [012 - Active Learning](012-engineered_features.qmd)
Active learning strategies for experimental design.
