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Selection (evolutionary Algorithm)
Selection is a genetic operator in an evolutionary algorithm (EA). An EA is a metaheuristic inspired by biological evolution and aims to solve challenging problems at least approximately. Selection has a dual purpose: on the one hand, it can choose individual genomes from a population for subsequent breeding (e.g., using the crossover operator). In addition, selection mechanisms are also used to choose candidate solutions (individuals) for the next generation. The biological model is natural selection. Retaining the best individual(s) of one generation unchanged in the next generation is called ''elitism'' or ''elitist selection''. It is a successful (slight) variant of the general process of constructing a new population. The basis for selection is the quality of an individual, which is determined by the fitness function. In memetic algorithms, an extension of EA, selection also takes place in the selection of those offspring that are to be improved with the help of a meme ( ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Operator
A genetic operator is an Operator (programming), operator used in evolutionary algorithms (EA) to guide the algorithm towards a solution to a given problem. There are three main types of operators (Mutation (evolutionary algorithm) , mutation, Crossover (evolutionary algorithm), crossover and selection (evolutionary algorithm), selection), which must work in conjunction with one another in order for the algorithm to be successful. Genetic operators are used to create and maintain genetic diversity (mutation operator), combine existing solutions (also known as chromosome (evolutionary algorithm), chromosomes) into new solutions (crossover) and select between solutions (selection). The classic representatives of evolutionary algorithms include Genetic algorithm, genetic algorithms, Evolution strategy, evolution strategies, genetic programming and evolutionary programming. In his book discussing the use of genetic programming for the optimization of complex problems, computer scienti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stochastic Universal Sampling
Stochastic universal sampling (SUS) is a selection technique used in evolutionary algorithm Evolutionary algorithms (EA) reproduce essential elements of the biological evolution in a computer algorithm in order to solve "difficult" problems, at least Approximation, approximately, for which no exact or satisfactory solution methods are k ...s for selecting potentially useful solutions for recombination. It was introduced by James Baker. SUS is a development of fitness proportionate selection (FPS) which exhibits no bias and minimal spread. Where FPS chooses several solutions from the population by repeated random sampling, SUS uses a single random value to sample all of the solutions by choosing them at evenly spaced intervals. This gives weaker members of the population (according to their fitness) a chance to be chosen. FPS can have bad performance when a member of the population has a really large fitness in comparison with other members. Using a comb-like ruler, SUS start ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sexual Selection
Sexual selection is a mechanism of evolution in which members of one sex mate choice, choose mates of the other sex to mating, mate with (intersexual selection), and compete with members of the same sex for access to members of the opposite sex (intrasexual selection). These two forms of selection mean that some individuals have greater reproductive success than others within a population, for example because they are more Animal sexual behaviour, attractive or prefer more attractive partners to produce offspring. Successful males benefit from frequent mating and monopolizing access to one or more fertile females. Females can maximise the return on the energy they invest in reproduction by selecting and mating with the best males. The concept was first articulated by Charles Darwin who wrote of a "second agency" other than natural selection, in which competition between mate candidates could lead to speciation. The theory was given a mathematical basis by Ronald Fisher in the e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Natural Selection
Natural selection is the differential survival and reproduction of individuals due to differences in phenotype. It is a key mechanism of evolution, the change in the Heredity, heritable traits characteristic of a population over generations. Charles Darwin popularised the term "natural selection", contrasting it with selective breeding, artificial selection, which is intentional, whereas natural selection is not. Genetic diversity, Variation of traits, both Genotype, genotypic and phenotypic, exists within all populations of organisms. However, some traits are more likely to facilitate survival and reproductive success. Thus, these traits are passed the next generation. These traits can also become more Allele frequency, common within a population if the environment that favours these traits remains fixed. If new traits become more favoured due to changes in a specific Ecological niche, niche, microevolution occurs. If new traits become more favoured due to changes in the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Tournament Selection
Tournament selection is a method of selecting an individual from a population of individuals in a evolutionary algorithm. Tournament selection involves running several "tournaments" among a few individuals (or "chromosomes") chosen at random from the population. The winner of each tournament (the one with the best fitness) is selected for crossover. '' Selection pressure'' is then a probabilistic measure of a chromosome's likelihood of participation in the tournament based on the participant selection pool size, is easily adjusted by changing the tournament size. The reason is that if the tournament size is larger, weak individuals have a smaller chance to be selected, because, if a weak individual is selected to be in a tournament, there is a higher probability that a stronger individual is also in that tournament. Pseudo Code The tournament selection method may be described in pseudo code: choose k (the tournament size) individuals from the population at random choose the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stochastic Universal Sampling
Stochastic universal sampling (SUS) is a selection technique used in evolutionary algorithm Evolutionary algorithms (EA) reproduce essential elements of the biological evolution in a computer algorithm in order to solve "difficult" problems, at least Approximation, approximately, for which no exact or satisfactory solution methods are k ...s for selecting potentially useful solutions for recombination. It was introduced by James Baker. SUS is a development of fitness proportionate selection (FPS) which exhibits no bias and minimal spread. Where FPS chooses several solutions from the population by repeated random sampling, SUS uses a single random value to sample all of the solutions by choosing them at evenly spaced intervals. This gives weaker members of the population (according to their fitness) a chance to be chosen. FPS can have bad performance when a member of the population has a really large fitness in comparison with other members. Using a comb-like ruler, SUS start ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fitness Proportionate Selection
Fitness proportionate selection, also known as roulette wheel selection or spinning wheel selection, is a selection technique used in evolutionary algorithms for selecting potentially useful solutions for recombination. Method In fitness proportionate selection, as in all selection methods, the fitness function assigns a fitness to possible solutions or chromosomes. This fitness level is used to associate a probability of selection with each individual chromosome. If f_i is the fitness of individual i in the population, its probability of being selected is : p_i = \frac, where N is the number of individuals in the population. This could be imagined similar to a Roulette wheel in a casino. Usually a proportion of the wheel is assigned to each of the possible selections based on their fitness value. This could be achieved by dividing the fitness of a selection by the total fitness of all the selections, thereby normalizing them to 1. Then a random selection is made similar to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Population Model (evolutionary Algorithm)
The population model of an evolutionary algorithm (EA) describes the structural properties of its population to which its members are subject. A population is the set of all proposed solutions of an EA considered in one iteration, which are also called ''individuals'' according to the biological role model. The individuals of a population can generate further individuals as offspring with the help of the genetic operators of the procedure. The simplest and widely used population model in EAs is the ''global'' or ''Panmixia, panmictic model'', which corresponds to an unstructured population. It allows each individual to choose any other individual of the population as a partner for the production of offspring by Crossover (genetic algorithm), crossover, whereby the details of the Selection (genetic algorithm), selection are irrelevant as long as the Fitness function, fitness of the individuals plays a significant role. Due to global mate selection, the genetic information of even sl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
