plotParetoGrid

Plot the Pareto front and set for 2 variables 2 objectives test problems with evaluations on a grid.

Gaussian process regression models, a.k.a. Kriging models, are
applied to global multi-objective optimization of black-box functions.
Multi-objective Expected Improvement and Step-wise Uncertainty Reduction
sequential infill criteria are available. A quantification of uncertainty
on Pareto fronts is provided using conditional simulations.

Mickael Binois

GPareto

Gaussian Processes for Pareto Front Estimation and Optimization

plotParetoGrid function

<dl><dt>fname</dt>
<dd>name of the function considered,</dd>
<dt>xlim, ylim</dt>
<dd>numeric vectors of length 2, giving the <code>x</code> and <code>y</code> coordinates ranges, default is <code>[0,1] x [0,1]</code>,</dd>
<dt>n.grid</dt>
<dd>number of divisions of the grid in each dimension.</dd></dl>

Arguments

Visualisation of Pareto front and set — plotParetoGrid

<dl>

<dt>fname</dt>
<dd>name of the function considered,</dd>


<dt>xlim, ylim</dt>
<dd>numeric vectors of length 2, giving the <code>x</code> and <code>y</code> coordinates ranges, default is <code>[0,1] x [0,1]</code>,</dd>


<dt>n.grid</dt>
<dd>number of divisions of the grid in each dimension.</dd>

</dl>

plotParetoGrid: Visualisation of Pareto front and set

Description

Usage

Arguments

Examples