Description Usage Arguments Details Value Author(s) References Examples

The function initializes the EMMA procedure. It generates the experimental space and selects the initial set of experimental points, namely the initial set of experiments to be performed. Random sampling is used for that purpose.

1 2 |

`in.name` |
A vector containing the names of the input variables (factors). |

`nlev` |
A numeric vector of the same length as |

`lower` |
A numeric vector of the same length as |

`upper` |
A numeric vector of the same length as |

`out.name` |
A vector containing the name(s) of the output/response variable(s). |

`nd` |
Number of experimental points to be selected when |

`fn1` |
The first function to be optimised; use |

`fn2` |
The first function to be optimised; use |

`fn3` |
The third function to be optimised; use |

`fn4` |
The fourth function to be optimised; use |

At the moment the function does not implement the use of constraints for the factors. Unfeasible
experiments are easily excluded by manipulating the matrix `xspace`

in an object of
class `emmat0`

.

An object of class `emmat0`

with the components listed below:

`xpop ` |
Experimental points investigated. |

`ypop` |
Response values observed at the experimental points investigated. |

`xspace ` |
Experimental region. |

`yspace ` |
Response values that have been either observed or predicted. Observed response values
are stored also in |

`opt ` |
Indicates if each single function is either minimized ('mn') or maximized ('mx'). |

`nd ` |
Number of experimental points selected initially ( |

`na ` |
Number of experimental points selected in subsequent iterations ( |

`tested ` |
ID of the tested experimental points. |

`time ` |
Current time instant of the EMMA procedure. |

`opt ` |
Indicates if each single objective function is either minimized ('mn') or maximized ('mx'). |

Laura Villanova, Kate Smith-Miles and Rob J Hyndman

Villanova L., Falcaro P., Carta D., Poli I., Hyndman R., Smith-Miles K. (2010) 'Functionalization of Microarray Devices: Process Optimization Using a Multiobjective PSO and Multiresponse MARS Modelling', IEEE CEC 2010, DOI: 10.1109/CEC.2010.5586165

Carta D., Villanova L., Costacurta S., Patelli A., Poli I., Vezzu' S., Scopece P., Lisi F., Smith-Miles K., Hyndman R. J., Hill A. J., Falcaro P. (2011) 'Method for Optimizing Coating Properties Based on an Evolutionary Algorithm Approach', Analytical Chemistry 83 (16), 6373-6380.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
#########################
## 1 response variable ##
#########################
## define the problem variables
in.name <- c("x1","x2")
nlev <- c(20, 20)
lower <- c(-2.048, -2.048)
upper <- c(2.048, 2.048)
out.name <- "y"
## identify the initial set of experimental runs (initialization)
## simulated problem (with known objective function)
tn <- emmat0(in.name, nlev, lower, upper, out.name, nd = 10, fn1 = ackley)
## applicative problem (with unknown objective function)
tn <- emmat0(in.name, nlev, lower, upper, out.name, nd = 10)
## perform the experiments in \code{tn$xpop} and measure the
## response values, then load in \code{tn$ypop} the measured
## response values
# tn$ypop<-...
##########################
## 2 response variables ##
##########################
in.name <- c("x1", "x2")
nlev <- c(20, 20)
lower <- c(-3, -3)
upper <- c(3, 3)
out.name <- c("y1", "y2")
weight <- c(0.2, 0.8)
C <- 10
pr.mut <- c(0.1, 0.07, 0.04, rep(0.01, C-3))
tn <- emmat0(in.name, nlev, lower, upper, out.name, nd = 10, fn1 = ackley,
fn2 = peaks)
``` |

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