Now, the function M of McBAR differs from R in the following.
It is of interest to determine the performance of McBAR when the centroid is computed using a median, in which case the centroid is renamed as medoid and McBAR as MedianBAR.
This section provides information on techniques, other than CBAR, McBAR, and MedianBAR, that are utilized to legitimize some components of McBAR, that is, to achieve goal 1 in the Introduction.
As mentioned in Introduction, goal 1 in this paper is to legitimize some subalgorithms of McBAR. These subalgorithms are items 2 to 5 of those enumerated in Section 3.4.
Based on the descriptions in Sections 3.3 and 3.4 of the subalgorithms of CBAR and McBAR, respectively, GIBAR differs from McBAR in using R, in the mapping F of task IDs in genotypes, and in the maintenance of system S at mapped mode.
(2) Mapping of task IDs for centroid-based adaptation with stochastic repair (McBAS) differs from McBAR only in using random centroid repair R instead of the minimal centroid repair M.
(3) Mapping of task IDs for centroid-based adaptation (McBA) differs from McBAR in randomly selecting genotypes from the set Gnds(t - 1) of genotypes that correspond to the nondominated solutions to the last subproblem [[phi].sup.2.sub.t - i], instead of generating genotypes through SSGS, to form the Rnd(f) component of the initial population in (34), where t corresponds to the current subproblem 02 being solved.
(4) Mapping of task IDs for centroid-based adaptation with medoids (MedianBAR) differs from McBAR only in using the median (defined in (36)) instead of the mean to compute for the centroid R(k) in (34).
(5) EDA/[[PHI].sup.2] is defined in Section 3.5.1 and differs from McBAR in its use of sampling and the estimation of a probability matrix (described in Section 2.5), instead of using mutation and crossover operators, to form the next generation offspring in its evolutionary process.
Note that the resulting C(t) is no longer a set of centroids but rather of genotypes of Gnds(t - 1).Thus, NDLPOP differs from McBAR in being an explicit memory-based approach.
It is also of interest to compare the performance of McBAR and the previously enumerated techniques to those of the following techniques.
Techniques that apply the mapping function F, such as McBAS, McBA, McBAR, and MedianBAR, are classified as variants (of McBAR) and the rest of techniques in T as nonvariants.