Comparison of breeding strategies for the creation of a synthetic pig line

Creating a new synthetic line by crossbreeding means complementary traits from pure breeds can be combined in the new population. Although diversity is generated during the crossbreeding stage, in this study, we analyze diversity management before selection starts. Using genomic and phenotypic data from animals belonging to the first generation (G0) of a new line, different simulations were run to evaluate diversity management during the first generations of a new line and to test the effects of starting selection at two alternative times, G3 and G4. Genetic diversity was characterized by allele frequency, inbreeding coefficients based on genomic and pedigree data, and expected heterozygosity. Breeding values were extracted at each generation to evaluate differences in starting selection at G3 or G4. All simulations were run for ten generations. A scenario with genomic data to manage diversity during the first generations of a new line was compared with a random and a selection scenario. As expected, loss of diversity was higher in the selection scenario, while the scenario with diversity control preserved diversity. We also combined the diversity management strategy with different selection scenarios involving different degrees of diversity control. Our simulation results show that a diversity management strategy combining genomic data with selection starting at G4 and a moderate degree of diversity control generates genetic progress and preserves diversity.

DMO-QPSO: A Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithm Based on Decomposition with Diversity Control

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving multi-objective problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) algorithm with the MOEA/D framework in order to make the QPSO be able to solve MOPs effectively, with the advantage of the QPSO being fully used. We also employ a diversity controlling mechanism to avoid the premature convergence especially at the later stage of the search process, and thus further improve the performance of our proposed algorithm. In addition, we introduce a number of nondominated solutions to generate the global best for guiding other particles in the swarm. Experiments are conducted to compare the proposed algorithm, DMO-QPSO, with four multi-objective particle swarm optimization algorithms and one multi-objective evolutionary algorithm on 15 test functions, including both bi-objective and tri-objective problems. The results show that the performance of the proposed DMO-QPSO is better than other five algorithms in solving most of these test problems. Moreover, we further study the impact of two different decomposition approaches, i.e., the penalty-based boundary intersection (PBI) and Tchebycheff (TCH) approaches, as well as the polynomial mutation operator on the algorithmic performance of DMO-QPSO.

A Diversity Model Based on Dimension Entropy and Its Application to Swarm Intelligence Algorithm

The swarm intelligence algorithm has become an important method to solve optimization problems because of its excellent self-organization, self-adaptation, and self-learning characteristics. However, when a traditional swarm intelligence algorithm faces high and complex multi-peak problems, population diversity is quickly lost, which leads to the premature convergence of the algorithm. In order to solve this problem, dimension entropy is proposed as a measure of population diversity, and a diversity control mechanism is proposed to guide the updating of the swarm intelligence algorithm. It maintains the diversity of the algorithm in the early stage and ensures the convergence of the algorithm in the later stage. Experimental results show that the performance of the improved algorithm is better than that of the original algorithm.

Diversity Control in Evolutionary Computation using Asynchronous Dual-Populations with Search Space Partitioning

Diversity control is vital for effective global optimization using evolutionary computation (EC) techniques. This paper classifies the various diversity control policies in the EC literature. Many research works have attributed the high risk of premature convergence to sub-optimal solutions to the poor exploration capabilities resulting from diversity collapse. Also, excessive cost of convergence to optimal solution has been linked to the poor exploitation capabilities necessary to focus the search. To address this exploration-exploitation trade-off, this paper deploys diversity control policies that ensure sustained exploration of the search space without compromising effective exploitation of its promising regions. First, a dual-pool EC algorithm that facilitates a temporal evolution-diversification strategy is proposed. Then a quasi-random heuristic initialisation based on search space partitioning (SSP) is introduced to ensure uniform sampling of the initial search space. Second, for the diversity measurement, a robust convergence detection mechanism that combines a spatial diversity measure; and a population evolvability measure is utilised. It was found that the proposed algorithm needed a pool size of only 50 samples to converge to optimal solutions of a variety of global optimization benchmarks. Overall, the proposed algorithm yields a 33.34% reduction in the cost incurred by a standard EC algorithm. The outcome justifies the efficacy of effective diversity control on solving complex global optimization landscapes. Keywords: Diversity, exploration-exploitation tradeoff, evolutionary algorithms, heuristic initialisation, taxonomy.

The association between rs16917496 T/C polymorphism of SET8 gene and cancer risk in Asian populations: a meta-analysis

Epidemiological studies have demonstrated close associations between SET8 rs16917496 T/C polymorphism and cancer risk, but the results of published studies were not consistent. We therefore performed this meta-analysis to explore the associations between rs16917496 T/C polymorphism and cancer risk. Five online databases were searched. Odds ratios (ORs) with a 95% confidence interval (CI) were calculated to assess the association between rs16917496 T/C polymorphism and cancer risk. In addition, heterogeneity, accumulative, sensitivity analysis, and publication bias were conducted to check the statistical power. Overall, 13 publications involving 5878 subjects were identified according to included criteria. No significant cancer risk was observed in genetic model of SET8 rs16917496 T/C polymorphism in Asian populations (C vs. T: OR = 1.04, 95%CI = 0.88–1.23, P = 0.63%; TC vs. TT: OR = 1.17, 95%CI = 0.96–1.24, P = 0.11%; CC vs. TT: OR = 0.90, 95%CI = 0.60–1.37, P = 0.63; TC+CC vs. TT: OR = 1.11, 95%CI = 0.90–1.38, P = 0.33; CC vs. TT+TC: OR = 0.92, 95%CI = 0.65–1.30, P = 0.63). Furthermore, similar associations were found in the subgroup analysis of race diversity, control design, genotyping methods, and different cancer types. In summary, our meta-analysis indicated that the SET8 rs16917496 T/C polymorphism may not play a critical role in cancer development in Asian populations.