Population Diversity Measures Research Articles

Linkage disequilibrium and effective population size in Indian goat breeds

Genomic linkage disequilibrium (LD) is an important population diversity measure and an indicator determining the efficacy of genomic applications. The present population genomic study was carried out to comprehensively characterise the extent and pattern of LD in the two divergent Indian goat breeds namely, Attappady Black and Malabari goats reared under small holder production system. The LD was estimated using genome-wide single nucleotide polymorphism (SNP) marker data obtained by Illumina SNP50 goat BeadChip genotyping. The LD between adjacent SNPs across all autosomes measured as correlation coefficient (r2) in the studied population was low and mean ± standard deviation of LD were 0.0657 ± 0.0921 and 0.0661 ± 0.0911 for Attappady Black and Malabari goats, respectively. The LD varied significantly (p < 0.0001) among chromosomes, interaction between chromosome and breed and among marker intervals. The LD (r2) of 0.2 and 0.3 were extended up to 20 kb and 10 kb marker intervals, respectively. Based on the genomic data, Ne was estimated as 512.8198 in Malabari and 371.2220 in Attappady Black goats at 17 generations back. The weak LD observed in Indian goat breeds indicates increased diversity of these Indian goat breeds and also implies that denser SNP panel might be more beneficial for genomic applications.

Impact of population topology on particle swarm optimization and its variants: An information propagation perspective

Particle swarm optimization is one of the most effective optimization algorithms motivated by bird flocking behaviours. Population topology is a key aspect of particle swarm optimization research. However, after more than twenty years of research, the effects of the population topology are still poorly understood. Previous research has established that the information propagation speed determined by the population topology has an important impact on the algorithm performance; however, the impact of information propagation speed on particle swarm optimization and its variants has not yet been investigated. In this paper, information propagation in particle swarms is described and, hence, a method of simulating information propagation in particle swarms is introduced, which is used to obtain the information propagation speed. The correlation between the information propagation speed and algorithm performance is clarified through numerical simulation. The results show that the information propagation speed has a strong negative correlation with the population diversity of particle swarm optimization and its variants in the early iterations, regardless of the adopted test function and population diversity measure. The results also show that when optimizing problems with the same property, the impact of population topology on the optimization results of particle swarm optimization and variant algorithms is similar. Further more, this study provides some guidance on the population topology selection for particle swarm optimization and its variants. These findings contribute to our understanding of the impact of population topology on particle swarm optimization and its variants, and provide a basis for population topology selection for particle swarm optimization and its variants.

The Diversity Discrepancy, Or Why Your Classmates Are More Like You

Summary Diversity has many potential benefits, and increasing diversity in the classroom and the workplace is a high-priority goal for managers and policy-makers. Although there is nearly uniform support for increasing diversity, there is less agreement about how to quantify diversity. This note observes that there is a systematic bias between how diversity is perceived by an individual and the natural measures of population diversity. In particular, most individuals will find themselves among people who are “more like them” than the aggregate statistics indicate. For example, considering racial diversity in the classroom, do you measure diversity as the proportion of minority students in the average class, or the average proportion as experienced by a member of the minority? Using examples from the US Census and the Philadelphia Public Schools we show that these two metrics can be quite different, and we give a general mathematical treatment of this discrepancy.

Testing the effectiveness of genetic monitoring using genetic non-invasive sampling.

Effective conservation requires accurate data on population genetic diversity, inbreeding, and genetic structure. Increasingly, scientists are adopting genetic non‐invasive sampling (gNIS) as a cost‐effective population‐wide genetic monitoring approach. gNIS has, however, known limitations which may impact the accuracy of downstream genetic analyses. Here, using high‐quality single nucleotide polymorphism (SNP) data from blood/tissue sampling of a free‐ranging koala population (n = 430), we investigated how the reduced SNP panel size and call rate typical of genetic non‐invasive samples (derived from experimental and field trials) impacts the accuracy of genetic measures, and also the effect of sampling intensity on these measures. We found that gNIS at small sample sizes (14% of population) can provide accurate population diversity measures, but slightly underestimated population inbreeding coefficients. Accurate measures of internal relatedness required at least 33% of the population to be sampled. Accurate geographic and genetic spatial autocorrelation analysis requires between 28% and 51% of the population to be sampled. We show that gNIS at low sample sizes can provide a powerful tool to aid conservation decision‐making and provide recommendations for researchers looking to apply these techniques to free‐ranging systems.

Intensive monitoring for bees in North America: indispensable or improvident?

AbstractContinued reports of bee declines have prompted repeated calls for a national monitoring programme in the United States. Here, we argue that such a large‐scale surveillance monitoring programme would consume inordinate resources without providing the sought‐after data in a meaningful time period.Surveillance monitoring cannot provide reliable and timely measures of absolute or relative population size, species richness or diversity, host plant and nest site preferences or typical foraging ranges. In addition, the multitude of specimens captured by the passive traps commonly used to surveil overwhelm our identification capacity and, in some cases, may even accelerate species decline.Conversely, surveillance monitoring should be pursued when data can be collected non‐lethally for readily identifiable species or species groups (e.g., bumble bees) or when widely distributed sentinel plants can be observed (e.g., sunflowers). Lethal collections should continue at appropriate temporal intervals but only at a limited number of representative areas with accurate historic records.We advocate that emphasis be placed on a targeted, natural‐history approach aimed at addressing specific hypotheses and exploring solutions to conservation management problems. We already know enough about the drivers of bee decline to formulate and test potential solutions. While additional information is of value in understanding bee diversity, distributions and evolution, we do not need it to begin to take action; we should be focusing on the best ways to help bee species endure rather than attempting to accumulate even more evidence that they are in trouble.

7  The use of in vitro fermentation methods to simulate an equine cecal environment

The equine gastrointestinal (GI) microbiota is intimately related to the health and well-being of the horse. However, access to more proximal portions of the GIT, such as the cecum, are challenging in live horses. Surgical techniques which enable in vivo cecal sampling, such as cannulation, are difficult to perform and maintenance can be problematic. The objective of this study was to evaluate changes in the microbiome of cecal inoculum maintained in an anaerobe chamber or a chemostat, and of a fecal slurry maintained in an anaerobe chamber over 48h. Cecal and fecal samples were collected immediately upon death from an abattoir in QC CAN. Cecal and fecal material were transported on ice, anaerobically to the University of Guelph where 500mL of cecal fluid was used to inoculate a chemostat vessel (ch_c, n = 11) or 100mL was transferred to a vessel in an anaerobe chamber (an_c, n = 15). One hundred ml of 5% fecal slurry in sterile PBS was also maintained in an anaerobe chamber (an_f, n = 6). Sampling was performed at the time of vessel establishment (0h), after 24h and 48h of fermentation. Sequencing of the V4 region of the 16S rRNA gene was performed on an Illumina MiSeq. Bioinformatics analysis was run using mothur software. Measures of population richness (Chao1), evenness (Shannon Evenness), and diversity (Inverse Simpson) were analyzed between methods and over time using a repeated measures ANOVA in SAS 9.4. Measures of community membership (Jaccard) and structure (Bray-Curtis) were visualized using PCoA. No overall differences were noted between the systems with regards to measures of α diversity ( P > 0.05). However, richness changed in an_c over time (0h vs 24h and 48h, P < 0.05), and diversity as well as evenness changed in an_f over time (0h vs 24h and 48h, P < 0.05). Visual assessment of PCoA revealed a clustering of an_f samples. No clustering of an_c or ch_c samples was evident. The microbiome of cecal inoculum maintained in a chemostat vessel remains consistent over 48h based on measurements of α diversity. The microbiome of cecal inoculum maintained in an anaerobe chamber is also reasonably consistent over this time period. The microbiome of fecal inoculum maintained anaerobically shifts over 48h and fecal inoculum creates an inconsistent environment over time for in vitro use. It appears that the use of cecal inoculum in either a chemostat vessel, or if unavailable, an anaerobe chamber, will provide a stable community over 48h with respect to the composition of the microbiome for in vitro applications. Additional measures of microbe viability, as well as activity are required to accurately judge the stability of these environments.

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.

A Modified jSO Algorithm for Solving Constrained Engineering Problems

Proposing new strategies to improve the optimization performance of differential evolution (DE) is an important research study. The jSO algorithm was the announced winner of the Congress on Evolutionary Computation (CEC) 2017 competition on numerical optimization, and is the state-of-the-art algorithm in the SHADE (Success-History based Adaptive Differential Evolution) algorithm series. However, the jSO algorithm converges prematurely in the search space with different dimensions and is prone to falling into local optimum during evolution, as well as the problem of decreasing population diversity. In this paper, a modified jSO algorithm (MjSO) is proposed which is based on cosine similarity with parameter adaptation and a novel opposition-based learning restart mechanism incorporated with symmetry to address the above problems, respectively. Moreover, it is well known that parameter setting has a significant impact on the performance of the algorithm and the search process can be divided into two symmetrical parts. Hence, a parameter control strategy based on a symmetric search process is introduced in the MjSO. The effectiveness of these designs is supported by presenting a population clustering analysis, along with a population diversity measure to evaluate the performance of the proposed algorithm, three state-of-the-art DE variant algorithms (EBLSHADE, ELSHADE-SPACMA, and SALSHADE-cnEPSin) and two original algorithms (jSO and LSHADE) are compared with it, for solving 30 CEC’17 benchmark functions and three classical engineering design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitions in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios.

Success History-Based Adaptive Differential Evolution Using Turning-Based Mutation

Single objective optimization algorithms are the foundation of establishing more complex methods, like constrained optimization, niching and multi-objective algorithms. Therefore, improvements to single objective optimization algorithms are important because they can impact other domains as well. This paper proposes a method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on SHADE (Success-History based Adaptive Differential Evolution) in high dimensional search space. The proposed method is tested on the Single Objective Bound Constrained Numerical Optimization (CEC2020) benchmark sets in 5, 10, 15, and 20 dimensions for all SHADE, L-SHADE, and jSO algorithms. The effectiveness of the method is verified by population diversity measure and population clustering analysis. In addition, the new versions (Tb-SHADE, TbL-SHADE and Tb-jSO) using the proposed turning-based mutation get apparently better optimization results than the original algorithms (SHADE, L-SHADE, and jSO) as well as the advanced DISH and the jDE100 algorithms in 10, 15, and 20 dimensional functions, but only have advantages compared with the advanced j2020 algorithm in 5 dimensional functions.

High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem

To maintain the population diversity of genetic algorithms (GAs), we are required to employ an appropriate population diversity measure. However, commonly used population diversity measures designed for permutation problems do not consider the dependencies between the variables of the individuals in the population. We propose three types of population diversity measures that address high-order dependencies between the variables to investigate the effectiveness of considering high-order dependencies. The first is formulated as the entropy of the probability distribution of individuals estimated from the population based on an -th--order Markov model. The second is an extension of the first. The third is similar to the first, but it is based on a variable order Markov model. The proposed population diversity measures are incorporated into the evaluation function of a GA for the traveling salesman problem to maintain population diversity. Experimental results demonstrate the effectiveness of the three types of high-order entropy-based population diversity measures against the commonly used population diversity measures.

Measures of Nominal and Ordinal Population Diversity

A measure of population diversity across a set of categories such as racial and ethnic groups is presented that clearly demonstrates how the index captures deviations from a standard of maximum diversity. The measure is applied to subareas of a larger area such as census tracts and is employed to develop 2 alternative measures of neighborhood diversity for the larger area. This and other measures of diversity assume that the population is classified into a nominal, unordered set of categories. The distribution of the population into a set of rank-ordered categories introduces the additional element that the pattern of a distribution across the categories should be considered in assessing levels of diversity. A measure of ordinal diversity and corresponding measures of neighborhood ordinal diversity are presented to address this.

Distance based parameter adaptation for Success-History based Differential Evolution

Abstract This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the real-parameter single objective optimization (CEC2015 and CEC2017) benchmark sets in 10, 30, 50, and 100 dimensions for all SHADE, L-SHADE (SHADE with linear decrease of population size), and jSO algorithms. The proposed distance based parameter adaptation is designed to address the premature convergence of SHADE–based algorithms in higher dimensional search spaces to maintain a longer exploration phase. This design effectiveness is supported by presenting a population clustering analysis, along with a population diversity measure. Also, the new distance based algorithm versions (Db_SHADE, DbL_SHADE, and DISH) have obtained significantly better optimization results than their canonical counterparts (SHADE, L_SHADE, and jSO) in 30, 50, and 100 dimensional functions.

Services that add value in the city: The rise of the modern economy in Brazil

Recent technological advances have increased the importance of economic activities based on new production techniques with embedded, sophisticated services. In this sense, services offered as intermediate inputs for manufacturing firms that add value in this process constitute one of the main components of productivity gains and economic development. However, such service firms are not homogeneously distributed in space. This paper aims to investigate the spatial distribution of service firms that add value and compare it with the location of service firms that reduce cost. This is done by employing count data models (correcting for the excessive number of zeros whenever necessary) to identify the main factors associated with the location of existing and new firms in these sectors in Brazilian labor market areas. There is also an effort to control for the potential endogeneity of density measures. The main results show that despite the strong linkages with manufacturing, service firms do not necessarily pursue locations close to places that concentrate industrial employment, and they actually avoid to locate in neighboring areas of those who concentrate manufacturing activities. Higher population density, diversity and other measures of economic spatial concentration are much more relevant to determine the location of these firms.

Population diversity of particle swarm optimisation algorithms for solving multimodal optimisation problems

The aim of multimodal optimisation is to locate multiple peaks/optima in a single run and to maintain these found optima until the end of a run. In this paper, seven variants of particle swarm optimisation (PSO) algorithms are utilised to solve multimodal optimisation problems. The position diversity is utilised to measure the candidate solutions during the search process. Our goal is to measure the performance and effectiveness of variants of PSO algorithms and investigate why an algorithm performs effectively from the perspective of population diversity. Based on the experimental results, the conclusions could be made that the PSO with ring structure and social-only PSO with ring structure perform better than the other PSO variants on multimodal optimisation. From the population diversity measurement, it is shown that to obtain good performances on multimodal optimisation problems, an algorithm needs to balance its global search ability and solutions maintenance ability.

A niching evolutionary algorithm with adaptive negative correlation learning for neural network ensemble

This paper proposes a niching evolutionary algorithm with adaptive negative correlation learning, denoted as NEA_ANCL, for training the neural network ensemble. In the proposed NEA_ANCL, an adaptive negative correlation learning, in which the penalty coefficient λ is set to dynamically change during training, has been developed. The adaptation strategy is based on a novel population diversity measure with the purpose of appropriately controlling the trade-off between the diversity and accuracy in the ensemble. Further, a modified dynamical fitness sharing method is applied to preserve the diversity of population during training. The proposed NEA_ANCL has been evaluated on a number of benchmark problems and compared with related ensemble learning algorithms. The results show that our method can be used to design a satisfactory NN ensemble and outperform related works.

Contrasting results from molecular and pedigree-based population diversity measures in captive zebra highlight challenges facing genetic management of zoo populations.

Zoo conservation breeding programs manage the retention of population genetic diversity through analysis of pedigree records. The range of demographic and genetic indices determined through pedigree analysis programs allows the conservation of diversity to be monitored relative to the particular founder population for a species. Such approaches are based on a number of well-documented founder assumptions, however without knowledge of actual molecular genetic diversity there is a risk that pedigree-based measures will be misinterpreted and population genetic diversity misunderstood. We examined the genetic diversity of the captive populations of Grevy's zebra, Hartmann's mountain zebra and plains zebra in Japan and the United Kingdom through analysis of mitochondrial DNA sequences. Very low nucleotide variability was observed in Grevy's zebra. The results were evaluated with respect to current and historic diversity in the wild, and indicate that low genetic diversity in the captive population is likely a result of low founder diversity, which in turn suggests relatively low wild genetic diversity prior to recent population declines. Comparison of molecular genetic diversity measures with analogous diversity indices generated from the studbook data for Grevy's zebra and Hartmann's mountain zebra show contrasting patterns, with Grevy's zebra displaying markedly less molecular diversity than mountain zebra, despite studbook analysis indicating that the Grevy's zebra population has substantially more founders, greater effective population size, lower mean kinship, and has suffered less loss of gene diversity. These findings emphasize the need to validate theoretical estimates of genetic diversity in captive breeding programs with empirical molecular genetic data. Zoo Biol. 36:87-94, 2017. © 2016 Wiley Periodicals, Inc.

Length and Condition of Wild Chinook Salmon Smolts Influence Age at Maturity

AbstractAnadromous Chinook Salmon Oncorhynchus tshawytscha commonly mature at 3–5 years of age. Age at maturity is an important measure of population diversity, and has ecological importance. In the John Day River, a northeast Oregon tributary of the Columbia River, we tagged 24,240 wild Chinook Salmon smolts from 10 successive cohorts with passive integrated transponder tags and subsequently monitored their age at maturation. We used multinomial logistic regression and multimodel selection to assess the influence of biotic and abiotic factors on age at maturity. Smolt fork length, condition factor, and day of tagging all had significant associations with age at maturity. Conversely, we found no significant association between ocean conditions and age at maturity. The probability of age‐3 maturation increased concomitant with increased smolt length. In contrast, the probability of maturation at age‐5 was inversely related to smolt condition factor. Day of tagging had a significant effect, indicating that smolts tagged later in the spring had a higher probability of maturing at age 4 than age 3. Our data indicate that mean smolt size was greater when smolt abundance was lower, and larger smolts were ultimately associated with a younger age at maturity. For John Day Chinook Salmon, spawner escapements approaching or exceeding the stock–recruit replacement level appear to favor production of older Chinook Salmon.

Diversity Conserved Chaotic Artificial Bee Colony Algorithm based Brightness Preserved Histogram Equalization and Contrast Stretching Method

This study is organized into two parts. The first part introduces two image enhancement methods with the ability to preserve the original brightness of the image. These two methods are: optimal ranged brightness preserved contrast stretching (ORBPCS) method and weighted thresholded histogram equalization (WTHE) method. The efficiency of these two methods crucially depends on the method's associated parameters. To find the optimal values of the parameters Artificial Bee Colony (ABC) algorithm and a novel objective function have been employed in this study. The second part of this study mainly concentrates on the efficiency increment of ABC algorithm and to develop the proper objective functions to preserve the original brightness of the image. Some new mechanisms like population diversity measurement technique, use of chaotic sequence etc. are also introduced to enhance the efficiency of traditional ABC algorithm. The objective functions have been developed by using co-occurrence matrix and peak-signal to noise ratio (PSNR).

Variable structure control optimized by differential evolution approach applied to continuous stirred tank reactor

This paper considers the control design based on self-tuning discrete variable structure control (VSC) technique. A novel and systematic VSC design methodology is proposed, which integrates an estimator based on recursive least-squares algorithm, a discrete quasi-sliding surface, and an optimization method. Contrary to the trial and error tuning of the variable structure feedback gains reported in the literature, the controller parameters selection in this paper is done using an improved differential evolution (IDE) optimization approach. This paper introduces an improvement to the original differential evolution (DE) to enhance the convergence rate during the optimization cycle. Our IDE algorithm utilizes a population diversity measure to tune the mutation factor. The proposed IDE has been assessed to VSC design in order to control a continuous stirred tank reactor with nonlinear dynamic behavior. Simulation results reveal that the IDE is applicable and promising for the VSC design. Moreover, when compared to classical DE, IDE performed better in the task of VSC tuning in terms of the speed of convergence and solution quality.

Analytics on Fireworks Algorithm Solving Problems with Shifts in the Decision Space and Objective Space

Fireworks algorithms for solving problems with the optima shift in decision space and/or objective space are analyzed in this paper. The standard benchmark problems have several weaknesses in the research of swarm intelligence algorithms for solving single objective problems. The optimum is in the center of search range, and is the same at each dimension of the search space. The optimum shift in decision space and/or objective space could increase the difficulty of problem solving. A mapping strategy, modular arithmetic mapping, is utilized in the original fireworks algorithm to handle solutions out of search range. The solutions are implicitly guided to the center of search range for problems with symmetrical search range via this strategy. The optimization performance of fireworks algorithm on shift functions may be affected by this strategy. Four kinds of mapping strategies, which include mapping by modular arithmetic, mapping to the boundary, mapping to stochastic region, and mapping to limited stochastic region, are compared on problems with different dimensions and different optimum shift range. From experimental results, the fireworks algorithms with mapping to the boundary, or mapping to limited stochastic region obtain good performance on problems with the optimum shift. This is probably because the search tendency is kept in these two strategies. The definition of population diversity measurement is also proposed in this paper, from observation on population diversity changes, the useful information of fireworks algorithm solving different kinds of problems could be obtained.