Showing papers on "Selection (genetic algorithm) published in 2008"

Purposeful selection of variables in logistic regression

Abstract: Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process.

LOSITAN: A workbench to detect molecular adaptation based on a Fst-outlier method

Abstract: Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user. Here we present LOSITAN, a selection detection workbench based on a well evaluated F st -outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral F st ), provides data import and export functions, iterative contour smoothing and generation of graphics in a easy to use graphical user interface. LOSITAN is able to use modern multi-core processor architectures by locally parallelizing fdist, reducing computation time by half in current dual core machines and with almost linear performance gains in machines with more cores. LOSITAN makes selection detection feasible to a much wider range of users, even for large population genomic datasets, by both providing an easy to use interface and essential functionality to complete the whole selection detection process.

Concentration Inequalities and Model Selection

Abstract: Exponential and Information Inequalities- Gaussian Processes- Gaussian Model Selection- Concentration Inequalities- Maximal Inequalities- Density Estimation via Model Selection- Statistical Learning

Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface

Abstract: In motor imagery-based brain computer interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the common spatial pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank common spatial pattern (FBCSP) to perform autonomous selection of key temporal-spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.

Selection on codon bias

Abstract: In a wide variety of organisms, synonymous codons are used with different frequencies, a phenomenon known as codon bias. Population genetic studies have shown that synonymous sites are under weak selection and that codon bias is maintained by a balance between selection, mutation, and genetic drift. It appears that the major cause for selection on codon bias is that certain preferred codons are translated more accurately and/or efficiently. However, additional and sometimes maybe even contradictory selective forces appear to affect codon usage as well. In this review, we discuss the current understanding of the ways in which natural selection participates in the creation and maintenance of codon bias. We also raise several open questions: (i ) Is natural selection weak independently of the level of codon bias? It is possible that selection for preferred codons is weak only when codon bias approaches equilibrium and may be quite strong on genes with codon bias levels that are much lower and/or above equilibrium. (ii ) What determines the identity of the major codons? (iii ) How do shifts in codon bias occur? (iv) What is the exact nature of selection on codon bias? We discuss these questions in depth and offer some ideas on how they can be addressed using a combination of computational and experimental analyses.

Fast stable direct fitting and smoothness selection for generalized additive models

Abstract: Summary. Existing computationally efficient methods for penalized likelihood generalized addi tive model fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is per formed by optimizing 'whole model' criteria these problems disappear, but until now attempts to do this have employed finite-difference-based optimization schemes which are computationally inefficient and can suffer from false convergence. The paper develops the first computationally efficient method for direct generalized additive model smoothness selection. It is highly sta ble, but by careful structuring achieves a computational efficiency that leads, in simulations, to lower mean computation times than the schemes that are based on working model smoothness selection. The method also offers a reliable way of fitting generalized additive mixed models.

BLUP for phenotypic selection in plant breeding and variety testing

Abstract: Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method was originally developed in animal breeding for estimation of breeding values and is now widely used in many areas of research. It does not, however, seem to have gained the same popularity in plant breeding and variety testing as it has in animal breeding. In plants, application of mixed models with random genetic effects has up until recently been mainly restricted to the estimation of genetic and non- genetic components of variance, whereas estimation of genotypic values is mostly based on a model with fixed effects. This paper reviews recent developments in the application of BLUP in plant breeding and variety testing. These include the use of pedigree information to model and exploit genetic correlation among relatives and the use of flexible variance-covariance structures for genotype-by-environment interaction. We demonstrate that BLUP has good predictive accuracy compared to other procedures. While pedi- gree information is often included via the so-called numerator relationship matrix ðAÞ, we stress that it is frequently straightforward to exploit the same infor- mation by a simple mixed model without explicit reference to the A-matrix.

Measuring and comparing evolvability and constraint in multivariate characters

Abstract: The Lande equation forms the basis for our understanding of the short-term evolution of quantitative traits in a multivariate context It predicts the response to selection as the product of an additive genetic variance matrix and a selection gradient The selection gradient approximates the force and direction of selection, and the genetic variance matrix quantifies the role of the genetic system in evolution Attempts to understand the evolutionary significance of the genetic variance matrix are hampered by the fact that the majority of the methods used to characterize and compare variance matrices have not been derived in an explicit theoretical context We use the Lande equation to derive new measures of the ability of a variance matrix to allow or constrain evolution in any direction in phenotype space Evolvability captures the ability of a population to evolve in the direction of selection when stabilizing selection is absent Conditional evolvability captures the ability of a population to respond to directional selection in the presence of stabilizing selection on other trait combinations We then derive measures of character autonomy and integration from these evolvabilities We study the properties of these measures and show how they can be used to interpret and compare variance matrices As an illustration, we show that divergence of wing shape in the dipteran family Drosophilidae has proceeded in directions that have relatively high evolvabilities

Fitness of hatchery-reared salmonids in the wild

Abstract: Accumulating data indicate that hatchery fish have lower fitness in natural environments than wild fish. This fitness decline can occur very quickly, sometimes following only one or two generations of captive rearing. In this review, we summarize existing data on the fitness of hatchery fish in the wild, and we investigate the conditions under which rapid fitness declines can occur. The summary of studies to date suggests: nonlocal hatchery stocks consistently reproduce very poorly in the wild; hatchery stocks that use wild, local fish for captive propagation generally perform better than nonlocal stocks, but often worse than wild fish. However, the data above are from a limited number of studies and species, and more studies are needed before one can generalize further. We used a simple quantitative genetic model to evaluate whether domestication selection is a sufficient explanation for some observed rapid fitness declines. We show that if selection acts on a single trait, such rapid effects can be explained only when selection is very strong, both in captivity and in the wild, and when the heritability of the trait under selection is high. If selection acts on multiple traits throughout the life cycle, rapid fitness declines are plausible.

Overconfidence, ceo selection, and corporate governance

Abstract: We develop a model that shows that an overconfident manager, who sometimes makes value-destroying investments, has a higher likelihood than a rational manager of being deliberately promoted to CEO under value-maximizing corporate governance. Moreover, a risk-averse CEO's overconfidence enhances firm value up to a point, but the effect is nonmonotonic and differs from that of lower risk aversion. Overconfident CEOs also underinvest in information production. The board fires both excessively diffident and excessively overconfident CEOs. Finally, Sarbanes-Oxley is predicted to improve the precision of information provided to investors, but to reduce project investment.

Estimating Nonlinear Selection Gradients Using Quadratic Regression Coefficients: Double Or Nothing?

Abstract: The use of regression analysis has been instrumental in allowing evolutionary biologists to estimate the strength and mode of natural selection. Although directional and correlational selection gradients are equal to their corresponding regression coefficients, quadratic regression coefficients must be doubled to estimate stabilizing/disruptive selection gradients. Based on a sample of 33 papers published in Evolution between 2002 and 2007, at least 78% of papers have not doubled quadratic regression coefficients, leading to an appreciable underestimate of the strength of stabilizing and disruptive selection. Proper treatment of quadratic regression coefficients is necessary for estimation of fitness surfaces and contour plots, canonical analysis of the γ matrix, and modeling the evolution of populations on an adaptive landscape.

Selection cooperation in multi-source cooperative networks

Abstract: In a cooperative network with multiple potential relays and multiple simultaneous transmissions, we present selection cooperation wherein each source pairs with a single "best" relay. We analyze the outage probability of a simple and completely distributed selection scheme, requiring some feedback but no centralization, and show that it outperforms distributed space-time codes for networks with more than three relaying nodes. These gains are due to the more efficient use of power in networks using selection. We suggest two other more complex selection schemes based on increasing system intelligence and centralization, and show that for smaller network sizes their performance improvement over the simple selection scheme is not significant.

Multiobjective immune algorithm with nondominated neighbor-based selection

Abstract: Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.

Performance of Genomic Selection in Mice

Abstract: Selection plans in plant and animal breeding are driven by genetic evaluation. Recent developments suggest using massive genetic marker information, known as “genomic selection.” There is little evidence of its performance, though. We empirically compared three strategies for selection: (1) use of pedigree and phenotypic information, (2) use of genomewide markers and phenotypic information, and (3) the combination of both. We analyzed four traits from a heterogeneous mouse population (http://gscan.well.ox.ac.uk/), including 1884 individuals and 10,946 SNP markers. We used linear mixed models, using extensions of association analysis. Cross-validation techniques were used, providing assumption-free estimates of predictive ability. Sampling of validation and training data sets was carried out across and within families, which allows comparing across- and within-family information. Use of genomewide genetic markers increased predictive ability up to 0.22 across families and up to 0.03 within families. The latter is not statistically significant. These values are roughly comparable to increases of up to 0.57 (across family) and 0.14 (within family) in accuracy of prediction of genetic value. In this data set, within-family information was more accurate than across-family information, and populational linkage disequilibrium was not a completely accurate source of information for genetic evaluation. This fact questions some applications of genomic selection.

Lessons learnt from assembling screening libraries for drug discovery for neglected diseases.

Abstract: To enable the establishment of a drug discovery operation for neglected diseases, out of 2.3 million commercially available compounds 222 552 compounds were selected for an in silico library, 57 438 for a diverse general screening library, and 1 697 compounds for a focused kinase set. Compiling these libraries required a robust strategy for compound selection. Rules for unwanted groups were defined and selection criteria to enrich for lead-like compounds which facilitate straightforward structure–activity relationship exploration were established. Further, a literature and patent review was undertaken to extract key recognition elements of kinase inhibitors (“core fragments”) to assemble a focused library for hit discovery for kinases. Computational and experimental characterisation of the general screening library revealed that the selected compounds 1) span a broad range of lead-like space, 2) show a high degree of structural integrity and purity, and 3) demonstrate appropriate solubility for the purposes of biochemical screening. The implications of this study for compound selection, especially in an academic environment with limited resources, are considered.

How should variable selection be performed with multiply imputed data

Abstract: Multiple imputation is a popular technique for analysing incomplete data. Given the imputed data and a particular model, Rubin's rules (RR) for estimating parameters and standard errors are well established. However, there are currently no guidelines for variable selection in multiply imputed data sets. The usual practice is to perform variable selection amongst the complete cases, a simple but inefficient and potentially biased procedure. Alternatively, variable selection can be performed by repeated use of RR, which is more computationally demanding. An approximation can be obtained by a simple 'stacked' method that combines the multiply imputed data sets into one and uses a weighting scheme to account for the fraction of missing data in each covariate. We compare these and other approaches using simulations based around a trial in community psychiatry. Most methods improve on the naive complete-case analysis for variable selection, but importantly the type 1 error is only preserved if selection is based on RR, which is our recommended approach.

History, chance and adaptation during biological invasion: separating stochastic phenotypic evolution from response to selection.

Abstract: Introduced species often exhibit changes in genetic variation, population structure, selection regime and phenotypic traits as they colonize and expand into new ranges. For these reasons, species invasions are increasingly recognized as promising systems for studying adaptive evolution over contemporary time scales. However, changes in phenotypic traits during invasion occur under non-equilibrium demographic conditions and may reflect the influences of prior evolutionary history and chance events, as well as selection. We briefly review the evidence for phenotypic evolution and the role of selection during invasion. While there is ample evidence for evolutionary change, it is less clear if selection is the primary mechanism. We then discuss the likelihood that stochastic events shift phenotypic distributions during invasion, and argue that hypotheses of adaptation should be tested against appropriate null models. We suggest two experimental frameworks for separating stochastic evolution from adaptation: statistically accounting for phenotypic variation among putative invasion sources identified by using phylogenetic or assignment methods and by comparing estimates of differentiation within and among ranges for both traits and neutral markers (Q(ST) vs. F(ST)). Designs that incorporate a null expectation can reveal the role of history and chance in the evolutionary process, and provide greater insights into evolution during species invasions.

Decision making in equipment selection: an integrated approach with ahp and promethee

Abstract: Multi-attribute equipment selection is a very important issue for an effective manufacturing system, since the improper equipment selection might cause many problems affecting productivity, precision, flexibility and quality of the products negatively. On the other hand, selecting the best equipment among many alternatives is a multi-criteria decision making (MCDM) problem. In this study, an integrated approach which employs analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluations (PROMETHEE) together, is proposed for the equipment selection problem. The AHP is used to analyze the structure of the equipment selection problem and to determine weights of the criteria, and PROMETHEE method is used to obtain final ranking, and to make a sensitivity analysis by changing the weights. Proposed approach is applied to a problem of selecting milling machines to be purchased in an international company. Company management found the application and results satisfactory and implementable in their equipment selection decisions.

Selection: The Mechanism of Evolution

Abstract: Simple selection Selection on a single character Single episode of selection Selection of pre-existing variation Continued selection The Evolution of novelty Selection on several characters Selection acting on different components of fitness Selection in several environments Selection acting at different levels Autoselection Elements that utilize existing modes of transmission Elements that modify existing modes of transmission Social selection Selection within a single uniform population density-dependent selection Selection within a single diverse population frequency-dependent selection Selection among populations kin selection and group selection Coevolution Sexual selection.

Evolutionary transitions in floral color

Abstract: The tremendous diversity in flower color among angiosperms implies that there have been numerous evolutionary transitions in this character. The conventional wisdom is that a large proportion of these transitions reflect adaptation to novel pollinator regimes. By contrast, recent research suggests that many of these transitions may instead have been driven by selection imposed by nonpollinator agents of selection acting on pleiotropic effects of flower color genes. I evaluate the evidence for these alternative hypotheses and find that while there is circumstantial evidence consistent with each hypothesis, there are no definitive examples of flower color evolution conforming to either hypothesis. I also document four macroevolutionary trends in flower color evolution: color transitions rates are often asymmetrical; biases favoring loss of pigmentation or favoring gain of pigmentation are both observed, but bias favoring transition from blue to red flowers seems more common than the reverse bias; transition...

How does selection operate on whole-organism functional performance capacities? A review and synthesis

Abstract: Hypothesis: Natural and sexual selection should be stronger on whole-organism functional performance traits (sprinting, biting) than on correlated morphological variables. Organisms: Lizards, snakes, turtles, frogs, and fish (review of past field and laboratory studies). Field sites: Various (review of past field and laboratory studies). Methods: We reviewed existing literature on the nature and intensity of natural and sexual selection on whole-organism performance traits. We answer some key questions in regards to how selection operates on performance, and whether selection is stronger on performance compared with morphological traits. Results: We identified 23 studies that have quantified selection on performance. Natural and sexual selection were typically directional in nature, with a distinct preference for high rather than low values of performance. However, some studies uncovered no significant selection on performance, and there was also no evidence that selection was stronger on performance traits than morphological traits.

Modifying effects of phenotypic plasticity on interactions among natural selection, adaptation and gene flow

Abstract: Divergent natural selection, adaptive divergence and gene flow may interact in a number of ways. Recent studies have focused on the balance between selection and gene flow in natural populations, and empirical work has shown that gene flow can constrain adaptive divergence, and that divergent selection can constrain gene flow. A caveat is that phenotypic diversification may be under the direct influence of environmental factors (i.e. it may be due to phenotypic plasticity), in addition to partial genetic influence. In this case, phenotypic divergence may occur between populations despite high gene flow that imposes a constraint on genetic divergence. Plasticity may dampen the effects of natural selection by allowing individuals to rapidly adapt phenotypically to new conditions, thus slowing adaptive genetic divergence. On the other hand, plasticity may promote future adaptive divergence by allowing populations to persist in novel environments. Plasticity may promote gene flow between selective regimes by allowing dispersers to adapt to alternate conditions, or high gene flow may result in the selection for increased plasticity. Here I expand frameworks for understanding relationships among selection, adaptation and gene flow to include the effects of phenotypic plasticity in natural populations, and highlight its importance in evolutionary diversification.

Mutation-Selection Models of Codon Substitution and Their Use to Estimate Selective Strengths on Codon Usage

Abstract: Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we implement a few population genetics models of codon substitution that explicitly consider mutation bias and natural selection at the DNA level. Selection on codon usage is modeled by introducing codon-fitness parameters, which together with mutation-bias parameters, predict optimal codon frequencies for the gene. The selective pressure may be for translational efficiency and accuracy or for fine-tuning translational kinetics to produce correct protein folding. We apply the models to compare mitochondrial and nuclear genes from several mammalian species. Model assumptions concerning codon usage are found to affect the estimation of sequence distances (such as the synonymous rate d(S), the nonsynonymous rate d(N), and the rate at the 4-fold degenerate sites d(4)), as found in previous studies, but the new models produced very similar estimates to some old ones. We also develop a likelihood ratio test to examine the null hypothesis that codon usage is due to mutation bias alone, not influenced by natural selection. Application of the test to the mammalian data led to rejection of the null hypothesis in most genes, suggesting that natural selection may be a driving force in the evolution of synonymous codon usage in mammals. Estimates of selection coefficients nevertheless suggest that selection on codon usage is weak and most mutations are nearly neutral. The sensitivity of the analysis on the assumed mutation model is discussed.

Decision support for sustainable option selection in integrated urban water management

Abstract: Conventional urban water management practices aim to meet water supply-demands while conveying wastewater and stormwater away from urban settings. Alternative approaches which consider water demands to be manageable and wastewater and stormwater as valuable resources, although being increasingly sought, lack reliable site specific implementation methodologies. This paper describes the development of a decision support tool (termed the Urban Water Optioneering Tool (UWOT)) to facilitate the selection of combinations of water saving strategies and technologies and to support the delivery of integrated, sustainable water management for new developments. The tool is based on a water balance model which allows the investigation of interactions between the major urban water cycle streams. The model is informed by a knowledge library which is populated with technological options and information on their major characteristics and performance. The technology selection is driven by a GA algorithm allowing efficient exploration of the decision space. Quantitative and qualitative sustainability criteria and indicators are used to compare between alternative composite water management strategies while preserving the multiobjective nature of the problem. The tool has been successfully tested on a case study site in the UK, and the results are presented and discussed. It is suggested that ''optioneering'' tools will increasingly become part of urban water management planning toolkits as practice moves towards more decentralised, integrated, context-specific solutions to address issues of sustainability.

Neighborhood Effects on Economic Self‐Sufficiency: A Reconsideration of the Moving to Opportunity Experiment1

Abstract: This article revisits the Moving to Opportunity housing mobility experiment, which heretofore has not provided strong evidence to support the hypothesis of neighborhood effects on economic self‐sufficiency among adults. The authors undertake a conceptual and empirical analysis of the study’s design and implementation to gain a better understanding of the selection processes that occur within the study. The article shows that the study is potentially affected by selectivity at several junctures: in determining who complied with the program’s requirements, who entered integrated versus segregated neighborhoods, and who left neighborhoods after initial relocation. Furthermore, previous researchers have not found an experimental treatment effect on adult economic self‐sufficiency, relative to controls. The authors propose an alternative approach that involves measuring the cumulative amount of time spent in different neighborhood environments. With this method, they find evidence that neighborhood is associat...

Variable Selection in Semiparametric Regression Modeling.

Abstract: In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and select significant variables for parametric portion. Thus, it is much more challenging than that for parametric models such as linear models and generalized linear models because traditional variable selection procedures including stepwise regression and the best subset selection require model selection to nonparametric components for each submodel. This leads to very heavy computational burden. In this paper, we propose a class of variable selection procedures for semiparametric regression models using nonconcave penalized likelihood. The newly proposed procedures are distinguished from the traditional ones in that they delete insignificant variables and estimate the coefficients of significant variables simultaneously. This allows us to establish the sampling properties of the resulting estimate. We first establish the rate of convergence of the resulting estimate. With proper choices of penalty functions and regularization parameters, we then establish the asymptotic normality of the resulting estimate, and further demonstrate that the proposed procedures perform as well as an oracle procedure. Semiparametric generalized likelihood ratio test is proposed to select significant variables in the nonparametric component. We investigate the asymptotic behavior of the proposed test and demonstrate its limiting null distribution follows a chi-squared distribution, which is independent of the nuisance parameters. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedures.