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SPOT (version 2.0.3)

Sequential Parameter Optimization Toolbox

Description

A set of tools for model based optimization and tuning of algorithms. It includes surrogate models, optimizers and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources.

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Version

Install

install.packages('SPOT')

Monthly Downloads

232

Version

2.0.3

License

GPL (>= 2)

Maintainer

Martin Zaefferer

Last Published

May 14th, 2018

Functions in SPOT (2.0.3)

corrnoisykriging

Correlation: Noisy Kriging

Correlation: Expg

Correlation: Spherical

Correlation: Exp

Correlation: Noisy Gauss

Correlation: Lin

Correlation: Cubic

calculationMothes

Cyclone Simulation: Mothes

Fix model parameters DACE

Wrapper for Maximum Likelihood Estimation

krigingLikelihood

Calculate negative log-likelihood

normalizeMatrix

Normalize design

Descent RSM model

DACE objective function

Gas Sensor Data

Correlation: Kriging

Correlation: Gauss

Objective function - Cyclone Simulation: Barth/Muschelknautz

Sphere Test Function

Prepare DACE fit

daceStartParameters

Start parameter setup DACE

Minimization by GENetic Optimization Using Derivatives

Latin Hypercube Design Generator

Normalized LHD Design

normalizeMatrix2

Normalize design 2

Interpolated plot

designUniformRandom

Uniform Design Generator

Correlation: Spline

Evaluate DACE fit

duplicateAndReplicateHandling

Handle Duplicates and Replicates

Minimization by Differential Evolution

Minimization by L-BFGS-B

Evolution Strategy

Surface plot of a model

Surface plot of a function

predict.spotRandomForest

Prediction method for random forest

expectedImprovement

Expected Improvement

Minimization by Latin Hypercube Sampling

Print Function DACE Kriging

objectiveFunctionEvaluation

Objective Function Evaluation

predictKrigingReinterpolation

Predict Kriging Model (Re-interpolating)

Minimization by NLOPT

repairNonNumeric

Repair Non-numeric Values

Optimal Computing Budget Allocation

predict.kriging

Predict Kriging Model

print.spotLOESS

Print method for loess model

predict.ensembleStack

Predict Stacked Ensemble

predict.spotLOESS

Predict loess model

print.spotRandomForest

Print method for random forest

predict.spotLinearModel

Prediction method for linear model

predict.spotRSM

Predict RSM model

Regression: Regpoly0

Evolution Strategy Implementation

spotAlgEsDominantReco

spotAlgEsDominantReco

print.spotLinearModel

Print method for linear model

spotAlgEsMarriage

spotAlgEsGetSuccessRate

get Success Rate

spotAlgEsInterReco

spotAlgEsInterReco

Termination hps

spotAlgEsMarriageWithReplace

Marriage with replace

spotAlgEsInterRecoBeSw02

spotAlgEsInterRecoBeSw02

spotAlgEsStratMutation

spotAlgEsStratMutation

Default Control list for spot

spotAlgEsTermination

Print method for RSM model

Backslash operator.

Regression: Regpoly1

Regression: Regpoly2

Sequential Parameter Optimization

spotAlgEsObjMutation

spotAlgEsObjMutation

spotAlgEsIndividualInitial

Individual Initialization

spotAlgEsSelection

spotAlgEsSelection

spotAlgEsInitParentPop

Initialize Parent Population

Sequential Parameter Optimization Main Loop

Function Evaluation Wrapper

buildEnsembleStack

Ensemble: Stacking

buildKrigingDACE

Build DACE model

Build Response Surface Model

calculationBarthMuschelknautz

Cyclone Simulation: Barth/Muschelknautz

Linear Model Interface

Build LOESS Model

buildRandomForest

Random Forest Interface

Build Kriging Model

Sequential Parameter Optimization Toolbox