# predict_front: Predicted Pareto front In moko: Multi-Objective Kriging Optimization

## Description

This function creates a predicted pareto front based on the mean of Kriging models. The predicted mean of each objective and constraint is passed to the `nsga2` algorithm that builds .

## Usage

 `1` ```predict_front(model, lower, upper, control = NULL, modelcontrol = NULL) ```

## Arguments

 `model` Object of class `mkm`. `lower` Vector of lower bounds for the variables to be optimized over (default: 0 with length `model@d`). `upper` Vector of upper bounds for the variables to be optimized over (default: 1 with length `model@d`). `control` An optional list of control parameters that controls the optimization algorithm. One can control: `popsize`(default: `200`); `generations`(default: `30`); `cdist`(default: `1/model@d`); `mprob`(default: `15`); `mdist`(default: `20`). `modelcontrol` An optional list of control parameters to the `mkm` function (default: `object@control`).

## Value

object of class `ps` containing the predicted Pareto front

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```# ------------------------ # The Nowacki Beam # ------------------------ n <- 100 doe <- cbind(sample(0:n,n),sample(0:n,n))/n res <- t(apply(doe, 1, nowacki_beam)) model <- mkm(doe, res, modelcontrol = list(objective = 1:2, lower=c(0.1,0.1))) pf <- predict_front(model, c(0,0), c(1,1)) plot(nowacki_beam_tps\$set) points(pf\$set, col='blue') ```

### Example output ```
```

moko documentation built on July 2, 2020, 3:59 a.m.