Population Size Selection Research Articles

A detailed study about CDW-PSO, BWO and GM-CPSO methods on continuous function optimization

Gauss map based chaotic particle swarm optimization (GM-CPSO) is a state-of-the-art method involving the necessary chaotic map for PSO and has proved itself in global optimization, hybrid classifier design, etc. GM-CPSO can outperform recent techniques such as chaotic dynamic weight PSO (CDW-PSO), outclassing 20 optimization methods. However, the behavior of GM-CPSO on continuous characterized functions is unknown. As the main aim, this paper comprehensively determines the performance of GM-CPSO specifically on continuous function optimization. Black widow optimization (BWO) includes a non-stable population size that cannot be fixed. In BWO, the population can increase without any intervention during iterations, which prevents an objective comparison of the method with other methods. Thus, as the second aim, a new viewpoint on BWO population size selection is suggested for an objective comparison of the method. In various disciplines, stochastic optimization is inevitable to efficiently perform function optimization. Here, the necessary question concerns with which method the best convergence and performance can be achieved. As the third aim, we evaluate three state-of-the-art optimization methods to answer this question. To realize all of these aims, GM-CPSO is compared with CDW-PSO and BWO methods by using 10 continuous benchmark functions to perform a detailed comparison and reveal which one can achieve reliable scores on low-, middle-, and high-dimensional problems. Fitness-based comparisons, computation time analysis, and convergence-based evaluations are presented to determine the robustness of algorithms. As a result, GM-CPSO arises as the most remarkable method, especially for the middle-and high-dimensional continuous functions.

Financial feasibility of marker-aided selection in Douglas-fir

The land area required for a marker-aided selection (MAS) program to break-even (i.e., have equal costs and benefits) was estimated using computer simulation for coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in the Pacific Northwestern United States. We compared the selection efficiency obtained when using an index that included the phenotype and marker score with that obtained using only the phenotype. It was assumed that MAS was restricted to within-family selection, that the rotation age was 50 years, and that growth rate (h2= 0.25), tree form (h2= 0.25), and (or) wood density (h2= 0.45) were the objects of improvement. Several population quantitative trait loci (QTL) models, selection population sizes, and interest rates were considered. When large selection population sizes were employed (500 trees per family) MAS gave considerable increases in efficiency of within-family selection; however, results showed that the combination of small selection population sizes (100 trees per family) and many QTL of moderate effect could lead to losses in gain from MAS compared with phenotypic selection. For many reasonable selection scenarios and the simplified assumptions in our model, the land base required for breeding programs to break-even is smaller or near to the limit of those in place under operational breeding programs in the region. Considerably more research is needed to reasonably predict whether MAS would be cost-effective in practice. However, before some of the basic research needed to implement MAS can be done, organizations need to establish large blocks of full-sib families to allow for QTL identification.

Financial feasibility of marker-aided selection in Douglas-fir

The land area required for a marker-aided selection (MAS) program to break-even (i.e., have equal costs and benefits) was estimated using computer simulation for coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in the Pacific Northwestern United States. We compared the selection efficiency obtained when using an index that included the phenotype and marker score with that obtained using only the phenotype. It was assumed that MAS was restricted to within-family selection, that the rotation age was 50 years, and that growth rate ( h 2 = 0.25), tree form (h 2 = 0.25), and (or) wood density (h 2 = 0.45) were the objects of improvement. Several population quantitative trait loci (QTL) models, selection population sizes, and interest rates were considered. When large selection population sizes were em - ployed (500 trees per family) MAS gave considerable increases in efficiency of within-family selection; however, re- sults showed that the combination of small selection population sizes (100 trees per family) and many QTL of moderate effect could lead to losses in gain from MAS compared with phenotypic selection. For many reasonable se- lection scenarios and the simplified assumptions in our model, the land base required for breeding programs to break- even is smaller or near to the limit of those in place under operational breeding programs in the region. Considerably more research is needed to reasonably predict whether MAS would be cost-effective in practice. However, before some of the basic research needed to implement MAS can be done, organizations need to establish large blocks of full-sib families to allow for QTL identification.