Stochastic Universal Sampling

	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]
picture info	Statistically Uniform Statistics (from German: ', "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments. When census data (comprising every member of the target population) cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the syst ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	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 known. They belong to the class of Metaheuristic, metaheuristics and are a subset of Population Based Bio-Inspired Algorithms, population based bio-inspired algorithms and evolutionary computation, which itself are part of the field of computational intelligence. The mechanisms of biological evolution that an EA mainly imitates are reproduction, mutation, genetic recombination, recombination and natural selection, 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). Evolution of the population then takes place after the repeated application of the above operators. Evolutionary algorithms often perfor ... [...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]
	Roulette Wheel Selection Fitness proportionate selection, also known as roulette wheel selection or spinning wheel selection, is a Selection (evolutionary algorithm), selection technique used in evolutionary algorithms for selecting potentially useful solutions for Recombination (evolutionary algorithm), 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, ... [...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]
	Reward-based Selection Reward-based selection is a technique used in evolutionary algorithms for selecting potentially useful solutions for recombination. The probability of being selected for an individual is proportional to the cumulative reward obtained by the individual. The cumulative reward can be computed as a sum of the individual reward and the reward inherited from parents. Description Reward-based selection can be used within Multi-armed bandit framework for Multi-objective optimization to obtain a better approximation of the Pareto front. The newborn a'^ and its parents receive a reward r^, if a'^ was selected for new population Q^, otherwise the reward is zero. Several reward definitions are possible: 1. r^=1, if the newborn individual a'^ was selected for new population Q^. 2. r^ = 1 - \frac \mbox a'^ \in Q^ , where rank(a'^) is the rank of newly inserted individual in the population of \mu individuals. Rank can be computed using a well-known non-dominated sorting procedure. 3. ... [...More Info...] [...Related Items...] OR:* [Wikipedia] [Google] [Baidu]