Selection

1996 ◽  
Author(s):  
Thomas Bäck
Keyword(s):  
Model Selection ◽  
Evolutionary Algorithms ◽  
Evolutionary Biology ◽  
Disruptive Selection ◽  
Stabilizing Selection ◽  
Intermediate Phenotypes ◽  
Biological Reality ◽  
Survival And Reproduction ◽  
Implementation Models ◽  
Selection Operator

The genetic operators summarized in the set Ω, i.e. mutation and recombination (and probably others, e.g. inversion) create new individuals in a completely undirected way. In Evolutionary Algorithms, the selection operator plays a major role by imposing a direction on the search process, i.e. a clear preference of those individuals which perform better according to the fitness measure Φ. Selection is the only component of Evolutionary Algorithms where the fitness of individuals has an impact on the evolution process. The practical implementations of selection as discussed in sections 2.1.4, 2.2.4, and 2.3.4 seemingly contradict the biological viewpoint presented in section 1.1, where natural selection was emphasized not to be an active force but instead to be characterized by different survival and reproduction rates. However, artificial implementation models and biological reality are not necessarily contradicting each other. While in biological systems fitness can only be measured indirectly by differences in growth rates, fitness in Evolutionary Algorithms is a direct, well-defined and evaluable property of individuals. The biological struggle for existence (e.g. by predator-prey interactions, capabilities of somatic adaptation, and the particular physical properties of individuals) has no counterpart in computer implementations of standard Evolutionary Algorithms. Therefore, an artificial abstraction of these mechanisms can use fitness measures to determine survival and reproduction a posteriori, since the struggle for existence is completely hidden in the evaluation process of individuals. The fact that different survival and reproduction constitute selection is valid in both cases, but in Evolutionary Algorithms fitness is measurable and implies the survival and reproduction behavior, which is just opposite to biological reality. This is simply an implication of the fitness-centered intention which necessarily prevails design and application of these algorithms. Therefore, it is just a logic consequence to model selection as an active, fitness-based component of Evolutionary Algorithms. However, how to model selection is by no means a simple problem. In evolutionary biology, it is usually distinguished between stabilizing, directed, and disruptive selection (see [Fut90], pp. 174–175). In the case of stabilizing selection, intermediate phenotypes have best fitness values, while disruptive selection is characterized by two or more distinct phenotypes that are highly fit and by intermediate phenotypes of low fitness (this assumes an - albeit unknown - ordering of phenotypes).

Full Model Selection issue in temporal data through evolutionary algorithms: A brief review

2017 ◽  
Author(s):  
Nancy Perez-Castro ◽  
Aldo Marquez-Grajales ◽  
Hector Gabriel Acosta-Mesa ◽  
Efren Mezura-Montes
Keyword(s):  
Model Selection ◽  
Evolutionary Algorithms ◽  
Temporal Data ◽  
Full Model

scholarly journals Basic Conceptual Structure of Evolutionary Ecology

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rogério Parentoni Martins
Keyword(s):  
Evolutionary Biology ◽  
Evolutionary Ecology ◽  
Genotypic Variation ◽  
Structured Populations ◽  
Phenotypic Variance ◽  
Selective Agents ◽  
Survival And Reproduction ◽  
Environmental Variations ◽  
Successive Generations ◽  
Evolutionary Consequences

Concepts are linguistic structures with specific syntax and semantics used as sources of communicating ideas. Concepts can be simple (e.g., tree), complex (e.g., adaptation) and be part of a network of interactions that characterize an area of scientific research. The conceptual interrelationships and some evolutionary consequences upon which these interrelations are based will be addressed here. The evolutionary ecology is an area of research from the population evolutionary biology that deals mainly with the effect of positive natural selection on panmictic and structured populations. Environmental factors, conditions and variable resources in time and space, constitute the selective agents that act on the phenotypic and genotypic variation of populations in a single generation, could result in evolutionary adaptations, which are simply those traits that are most likely to confer survival and reproduction (evolutionary fitness) of the phenotypes that carry them in successive generations. The bases of adaptation are mainly genetic and transmitted vertically (classical Mendelian mechanisms) or horizontally (in the case of microorganisms). The phenotypic variance of the population is a conjoint consequence of the additive genotypic variance (heritability), nonadditive variance (dominance and epistasis), pleiotropy and the interaction between genotype and environment. The ability of the same genotype to respond to spatial environmental variations can result in phenotypic plasticity that manifests itself through reaction norms. The total phenotypic variation and its genetic and environmental components influence the ability of a population to evolve (evolvability).

scholarly journals Detecting selection with a genetic cross

2020 ◽  
Vol 117 (36) ◽  
pp. 22323-22330
Author(s):  
Hunter B. Fraser
Keyword(s):  
Skin Color ◽  
Evolutionary Biology ◽  
Neutral Evolution ◽  
Sign Test ◽  
Stabilizing Selection ◽  
Mrna Levels ◽  
Effective Population ◽  
Phenotypic Data ◽  
Genetic Cross ◽  
Selected Traits

Distinguishing which traits have evolved under natural selection, as opposed to neutral evolution, is a major goal of evolutionary biology. Several tests have been proposed to accomplish this, but these either rely on false assumptions or suffer from low power. Here, I introduce an approach to detecting selection that makes minimal assumptions and only requires phenotypic data from ∼10 individuals. The test compares the phenotypic difference between two populations to what would be expected by chance under neutral evolution, which can be estimated from the phenotypic distribution of an F2cross between those populations. Simulations show that the test is robust to variation in the number of loci affecting the trait, the distribution of locus effect sizes, heritability, dominance, and epistasis. Comparing its performance to the QTL sign test—an existing test of selection that requires both genotype and phenotype data—the new test achieves comparable power with 50- to 100-fold fewer individuals (and no genotype data). Applying the test to empirical data spanning over a century shows strong directional selection in many crops, as well as on naturally selected traits such as head shape in HawaiianDrosophilaand skin color in humans. Applied to gene expression data, the test reveals that the strength of stabilizing selection acting on mRNA levels in a species is strongly associated with that species’ effective population size. In sum, this test is applicable to phenotypic data from almost any genetic cross, allowing selection to be detected more easily and powerfully than previously possible.

Review Lecture Disruptive selection

1972 ◽  
Vol 182 (1067) ◽  
pp. 109-143 ◽  
Keyword(s):  
Gene Flow ◽  
Genetic Variance ◽  
Natural Populations ◽  
Disruptive Selection ◽  
Ecological Niches ◽  
Gene Exchange ◽  
Bisexual Species ◽  
Frequency Dependent ◽  
Ecological Conditions ◽  
Intermediate Phenotypes

A population is exposed to disruptive selection if more than one phenotype has optimal fitness and intermediate phenotypes have lower fitnesses. Maintenance of the two or more optima may depend upon their relative fitnesses being frequency dependent. Such selection may be expected in two contrasting types of situation. First the two or more optimal phenotypes may depend on one another as do the two sexes in a bisexual species. Secondly the optima may be set by heterogeneity of the environment. Then we may think in terms of a mosaic of ecological niches or a clinal situation, and may expect that gene flow will tend to promote convergence of the sub-populations while disruptive selection tends to promote their divergence. Disruptive selection may therefore be relevant both to the evolution and maintenance of polymorphisms and to the divergence of parts of populations one from another, under the influence of variation of ecological conditions within the range of gametic and/or zygotic dispersal. Disruptive selection has been shown to be capable of increasing phenotypic and genetic variance, of producing and maintaining polymorphisms, of causing divergence of sub-populations between which substantial gene exchange occurs, and of splitting a population into two which are genetically isolated from one another. These results are reviewed and their relevance to natural populations discussed.

Inferring natural selection in a fossil threespine stickleback

Paleobiology ◽  
2006 ◽  
Vol 32 (4) ◽  
pp. 562-577 ◽  
Author(s):  
Michael A. Bell ◽  
Matthew P. Travis ◽  
D. Max Blouw
Keyword(s):  
Natural Selection ◽  
Random Process ◽  
Genetic Drift ◽  
Fossil Record ◽  
Evolutionary Biology ◽  
Threespine Stickleback ◽  
Stabilizing Selection ◽  
Persistent Problem ◽  
Selection For ◽  
Lines Of Evidence

Inferring the causes for change in the fossil record has been a persistent problem in evolutionary biology. Three independent lines of evidence indicate that a lineage of the fossil stickleback fish Gasterosteus doryssus experienced directional natural selection for reduction of armor. Nonetheless, application to this lineage of three methods to infer natural selection in the fossil record could not exclude random process as the cause for armor change. Excluding stabilizing selection and genetic drift as the mechanisms for biostratigraphic patterns in the fossil record when directional natural selection was the actual cause is very difficult. Biostratigraphic sequences with extremely fine temporal resolution among samples and other favorable properties must be used to infer directional selection in the fossil record.

scholarly journals Cognitive innovations and the evolutionary biology of expertise

2017 ◽  
Vol 372 (1735) ◽  
pp. 20160427 ◽  
Author(s):  
Reuven Dukas
Keyword(s):  
Social Interactions ◽  
Cognitive Abilities ◽  
Evolutionary Biology ◽  
Environmental Changes ◽  
Internal Communication ◽  
Genetic Networks ◽  
Evolution Of Language ◽  
Survival And Reproduction ◽  
Biochemical Mechanisms ◽  
Communication And Coordination

Animal life can be perceived as the selective use of information for maximizing survival and reproduction. All organisms including bacteria and protists rely on genetic networks to build and modulate sophisticated structures and biochemical mechanisms for perceiving information and responding to environmental changes. Animals, however, have gone through a series of innovations that dramatically increased their capacity to acquire, retain and act upon information. Multicellularity was associated with the evolution of the nervous system, which took over many tasks of internal communication and coordination. This paved the way for the evolution of learning, initially based on individual experience and later also via social interactions. The increased importance of social learning also led to the evolution of language in a single lineage. Individuals' ability to dramatically increase performance via learning may have led to an evolutionary cycle of increased lifespan and greater investment in cognitive abilities, as well as in the time necessary for the development and refinement of expertise. We still know little, however, about the evolutionary biology, genetics and neurobiological mechanisms that underlie such expertise and its development. This article is part of the themed issue ‘Process and pattern in innovations from cells to societies’.

scholarly journals Fly wing evolution explained by a neutral model with mutational pleiotropy

2020 ◽  
Author(s):  
Daohan Jiang ◽  
Jianzhi Zhang
Keyword(s):  
Linear Correlation ◽  
Evolutionary Biology ◽  
Stabilizing Selection ◽  
Evolutionary Divergence ◽  
Phenotypic Evolution ◽  
Central Question ◽  
Evolutionary Patterns ◽  
Strong Linear Correlation ◽  
Mutational Variance

ABSTRACTTo what extent the speed of mutational production of phenotypic variation determines the rate of long-term phenotypic evolution is a central question in evolutionary biology. In a recent study, Houle et al. addressed this question by studying the mutational variation, microevolution, and macroevolution of locations of vein intersections on fly wings, reporting very slow phenotypic evolution relative to the rates of mutational input, high phylogenetic signals of these traits, and a strong, linear correlation between the mutational variance of a trait and its rate of evolution. Houle et al. examined multiple models of phenotypic evolution but found none consistent with all these observations. Here we demonstrate that the purported linear correlation between mutational variance and evolutionary divergence is an artifact. More importantly, patterns of fly wing evolution are explainable by a simple model in which the wing traits are neutral or neutral within a range of phenotypic values but their evolutionary rates are reduced because most mutations affecting these traits are purged owing to their pleiotropic effects on other traits that are under stabilizing selection. We conclude that the evolutionary patterns of fly wing morphologies are explainable under the existing theoretical framework of phenotypic evolution.

scholarly journals Indigenous Seeds, Seed Selection and Seed Bank for Sustainable Agriculture

2021 ◽  
Vol 04 (04) ◽  
pp. 13-26
Author(s):  
Bal Krishna Joshi ◽  
Keyword(s):  
Seed Bank ◽  
Selection Process ◽  
Conservation Status ◽  
Seed Banks ◽  
Disruptive Selection ◽  
Correlated Response ◽  
Mass Selection ◽  
Stabilizing Selection ◽  
Seed Selection ◽  
Pure Lines

Indigenous seeds are grown by the farmers over the years with a strong influence from local natural factors. Such seeds have a higher level of intrapopulation variations and the capacity of buffering the adverse factors. Understanding indigenous seeds along with their diversity are useful to diversify their uses, to assess conservation status, to know the factors making farming areas red zone, and to improve their performance. Selection is the simplest and most common method for the improvement of crop varieties. The variation must be created and maintained to impose selection. Different types of selection can be considered depending on the mode of reproduction of crops. Response to selection and correlated response are estimated to make the selection process more effective. Many different selection approaches can target either developing monomorphic or polymorphic varieties. There are five selection units and can be applied in five crop stages. Farmers’ criteria need to be considered during selection process. Based on the genotypic classes, there are three types of selection namely stabilizing selection, directional selection, and disruptive selection. The most simple and common selection methods are pure lines, mass selection, and class-bulking selection. Orthodox seeds in short, medium, and long-term storage facilities are conserved as a seed bank. Major types are household seed banks, community seed banks, national seeds, natural seed banks, and global seed banks. A seed bank is for assuring the availability of crop diversity for research, study, and production. The common works in seed banks are diversity collection, regeneration, characterization, multiplication, and distribution along with online database management.

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