SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization

TL;DR

SPOT is an R package that provides automatic and interactive tuning of optimization algorithms using sequential parameter optimization techniques, incorporating various statistical and machine learning models for effective analysis.

Contribution

The paper introduces SPOT, an R package that integrates multiple modeling approaches for systematic and interactive tuning of simulation and optimization algorithms.

Findings

Effective tuning of algorithms demonstrated

Supports diverse modeling techniques like Gaussian processes and random forests

Facilitates statistical analysis of optimization processes

Abstract

The sequential parameter optimization (SPOT) package for R is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. SPOT includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. This article exemplifies how SPOT can be used for automatic and interactive tuning.