Frequency-dependent selectionFrequency-dependent selection is an evolutionary process by which the fitness of a phenotype or genotype depends on the phenotype or genotype composition of a given population. In positive frequency-dependent selection, the fitness of a phenotype or genotype increases as it becomes more common. In negative frequency-dependent selection, the fitness of a phenotype or genotype decreases as it becomes more common. This is an example of balancing selection.
Genetic algorithmIn computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.
Stabilizing selectionStabilizing selection (not to be confused with negative or purifying selection) is a type of natural selection in which the population mean stabilizes on a particular non-extreme trait value. This is thought to be the most common mechanism of action for natural selection because most traits do not appear to change drastically over time. Stabilizing selection commonly uses negative selection (a.k.a. purifying selection) to select against extreme values of the character. Stabilizing selection is the opposite of disruptive selection.
Wireless mesh networkA wireless mesh network (WMN) is a communications network made up of radio nodes organized in a mesh topology. It can also be a form of wireless ad hoc network. A mesh refers to rich interconnection among devices or nodes. Wireless mesh networks often consist of mesh clients, mesh routers and gateways. Mobility of nodes is less frequent. If nodes constantly or frequently move, the mesh spends more time updating routes than delivering data.
Computational complexity theoryIn theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used.
Evolutionary algorithmIn computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function).
Protocol stackThe protocol stack or network stack is an implementation of a computer networking protocol suite or protocol family. Some of these terms are used interchangeably but strictly speaking, the suite is the definition of the communication protocols, and the stack is the software implementation of them. Individual protocols within a suite are often designed with a single purpose in mind. This modularization simplifies design and evaluation. Because each protocol module usually communicates with two others, they are commonly imagined as layers in a stack of protocols.
Balancing selectionBalancing selection refers to a number of selective processes by which multiple alleles (different versions of a gene) are actively maintained in the gene pool of a population at frequencies larger than expected from genetic drift alone. Balancing selection is rare compared to purifying selection. It can occur by various mechanisms, in particular, when the heterozygotes for the alleles under consideration have a higher fitness than the homozygote. In this way genetic polymorphism is conserved.
Comparison of email clientsThe following tables compare general and technical features of notable email client programs. Basic general information about the clients: creator/company, O/S, licence, & interface. Clients listed on a light purple background are no longer in active development. A brief digest of the release histories. The operating systems on which the clients can run natively (without emulation). What email and related protocols and standards are supported by each client. Information on what features each of the clients support.
Memetic algorithmA memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence. Memetic algorithms represent one of the recent growing areas of research in evolutionary computation.
Innate lymphoid cellInnate lymphoid cells (ILCs) are the most recently discovered family of innate immune cells, derived from common lymphoid progenitors (CLPs). In response to pathogenic tissue damage, ILCs contribute to immunity via the secretion of signalling molecules, and the regulation of both innate and adaptive immune cells. ILCs are primarily tissue resident cells, found in both lymphoid (immune associated), and non- lymphoid tissues, and rarely in the blood. They are particularly abundant at mucosal surfaces, playing a key role in mucosal immunity and homeostasis.
Directional selectionIn population genetics, directional selection, is a mode of negative natural selection in which an extreme phenotype is favored over other phenotypes, causing the allele frequency to shift over time in the direction of that phenotype. Under directional selection, the advantageous allele increases as a consequence of differences in survival and reproduction among different phenotypes. The increases are independent of the dominance of the allele, and even if the allele is recessive, it will eventually become fixed.
