02435naa a2200277 a 450000100080000000500110000800800410001902400400006010000220010024501350012226000090025752014950026665000200176165000220178165000230180365000230182665300220184965300220187165300400189365300240193365300330195765300250199070000200201570000190203577301030205421203272020-12-01       2019    bl uuuu     u00u1 u    #d7 a10.19080/IJESNR.2019.21.5560512DOI1 aMONTENEGRO, A. R.  aEffect of different mating systems on population structure and genetic progress of a simulated small flock.h[electronic resource]  c2019  aAbstract: Strategies to promote genetic progress or preserve genetic diversity in small populations may change due to population size. Higher inbreeding coefficients are associated to the use of breeding values predicted by mixed model methodology, which tends to score better animals within the best families. The reduced effective population size makes herds more susceptible to genetic drift and inbred matings. We compared three methodologies/software on simulated data that reproduced small-closed populations: Mate Selection (evolutionary differential), Gencont (Lagrange Multipliers) and SGRmate (linear programming). Algorithms optimized the objective function in order to achieve the higher genetic progress, but with an inbreeding coefficient of less than 10%, selecting the necessary number of sires and forming the reproductive pairs, except for Gencont, whose objective function was only to minimize the coancestry. All software generated populations with similar genetic progress. Mate Selection generated populations with the highest levels of inbreeding coefficients, similar to RANDOM, which presented best controlled mating between relatives. Gencont produced populations with intermediate levels of inbreeding. SGRmate maintained lowest levels of inbreeding due to higher number of sires selected and equal proportionality in combination of the pairs. Use of linear programming (SGRmate) was more efficient in maintaining the genetic diversity of small-closed populations  aAnimal genetics  aComputer software  aLinear programming  aSelection criteria  aAnimal population  aBreeding programs  aDifferential evolutionary algorithm  aMating optimization  aOptimal genetic contribution  aOptimization methods1 aSILVA, L. P. da1 aLOBO, R. N. B.  tInternational Journal of Environmental Sciences and Natural Resourcesgv. 21, n. 1, e556051, 2019.