Changes in Version 1.2
o The crossover function in the genetic algorithm has been modified by considering
the "grouping" version of this algorithm: instead of mixing chromosomes in an
indifferentiate way, groups of them in one parent (representing already aggregated
strata) are attributed to the other parent when generating a child, preserving their
composition. Moreover, parents are selected with a probability proportional to their
fitness (in previous version selection was completely at random). In many cases this
can greatly speed the convergence to an optimal solution.
o A new function 'adjustSize' has been added. It allows to adjust the sample size and
related allocation in strata on the basis of an externally indicated overall sample size.
The adjustment of the sample size is perfomed by increasing or decreasing it proportionally
in each optimized stratum.
o A new function 'buildFrameDF' has been added. It allows to create a 'sampling frame'
dataframe by indicating the dataset in which the information on all the units are
contained, the identifier, the X variables, the Y variable and the variable that
indicates the domains of interest.
o In function 'optimizeStrata' now the 'initialStrata' parameter is a vector, whose
length is equal to the number of different domains.
o All outputs are written to an .\output subdirectory.
Changes in Version 1.1
o Function 'memoise' from the same package (now required) is applied before
each evaluation in order to save processing time. This may largely increase
the efficiency of the algorithm
o A 'recode' function is applied on every generated solution in order
to recode a genotype of n genes with k<=n distinct alleles 1, 2, ..., k
in such a way that the distinct alleles of the recoded genotype appear
in the natural order 1, 2, ..., k. This avoids to consider as distinct two
solutions that are equivalent but make use of a different coding
Changes in Version 1.0-4
o Bug fix for old releases
Changes in Version 1.0-3
o Modified the output to the console or to the file of results: instead of all
the solutions, only the optimal value for each generation is visualised
o Now the visualisation of the trend in the optimal and mean values is optional:
the plot can be avoided by setting showPlot = FALSE when calling optimizeStrata.
It can be advisable when the number of iterations is very high
Changes in Version 1.0-2
o Bug fix for old releases.
Changes in Version 1.0-1
o Bug fix for old releases.
o The object returned by function "optimizeStrata" is no more a dataframe but a list:
* the first element of the list is the solution vector (solution$indices)
* the second element of the list is the dataframe containing aggregated strata
(solution$aggr_strata)
o In all the functions that previously produced .csv files and .pdf plots in the working
directory, as a default this is no more the current behaviour. To write these files, it
is now necessary to set the "writeFiles" flag to TRUE
Changes in Version 1.0
o Bug fix for old releases.
o Two new functions:
* "evalSolution", to evaluate the found solution in terms of expected target variables
precision and bias obtainable by samples drawn from the otpimized frame;
* "tuneParameters", to determine the best combination of values to assign to the parameters
necessary for the execution of the genetic algorithm used for the optimization of the frame
stratification.
o A demonstration on the use of the "tuneParameters" function is in the vignette "tuneParameters.pdf"
contained in the \inst\doc folder.