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Gaussian Adaptation
Gaussian adaptation (GA), also called normal or natural adaptation (NA) is an evolutionary algorithm designed for the maximization of manufacturing yield due to statistical deviation of component values of signal processing systems. In short, GA is a stochastic adaptive process where a number of samples of an ''n''-dimensional vector ''x'' 'x''T = (''x''1, ''x''2, ..., ''xn'')are taken from a multivariate Gaussian distribution, ''N''(''m'', ''M''), having mean ''m'' and moment matrix ''M''. The samples are tested for fail or pass. The first- and second-order moments of the Gaussian restricted to the pass samples are ''m*'' and ''M*''. The outcome of ''x'' as a pass sample is determined by a function ''s''(''x''), 0 < ''s''(''x'') < ''q'' ≤ 1, such that ''s''(''x'') is the probability that x will be selected as a pass sample. The average probability of finding pass samples (yield) is : Then t ... [...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] |
Hebbian Theory
Hebbian theory is a neuropsychological theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of neurons during the learning process. Hebbian theory was introduced by Donald Hebb in his 1949 book '' The Organization of Behavior.'' The theory is also called Hebb's rule, Hebb's postulate, and cell assembly theory. Hebb states it as follows: Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability. ... When an axon of cell ''A'' is near enough to excite a cell ''B'' and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that ''A''’s efficiency, as one of the cells firing ''B'', is increased. The theory is often summarized as "Neurons that fire togethe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Unit Of Selection
A unit of selection is a biological entity within the hierarchy of biological organization (for example, an entity such as: a self-replicating molecule, a gene, a cell, an organism, a group, or a species) that is subject to natural selection. There is debate among evolutionary biologists about the extent to which evolution has been shaped by selective pressures acting at these different levels.Okasha, S. (2006) ''Evolution and the levels of selection''. Oxford University Press. There is debate over the relative importance of the units themselves. For instance, is it group or individual selection that has driven the evolution of altruism? Where altruism reduces the fitness of ''individuals'', individual-centered explanations for the evolution of altruism become complex and rely on the use of game theory, for instance; see kin selection and group selection. There also is debate over the definition of the units themselves, and the roles for selection and replication, and whether ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CMA-ES
Covariance matrix adaptation evolution strategy (CMA-ES) is a particular kind of strategy for numerical optimization. evolution strategy, Evolution strategies (ES) are stochastic, Derivative-free optimization, derivative-free methods for numerical optimization of non-Linear map, linear or non-Convex function, convex continuous optimization problems. They belong to the class of evolutionary algorithms and evolutionary computation. An evolutionary algorithm is broadly based on the principle of biological evolution, namely the repeated interplay of variation (via recombination and mutation) and selection: in each generation (iteration) new individuals (candidate solutions, denoted as x) are generated by variation of the current parental individuals, usually in a stochastic way. Then, some individuals are selected to become the parents in the next generation based on their fitness or objective function value f(x). Like this, individuals with better and better f-values are generated ove ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stochastic Optimization
Stochastic optimization (SO) are optimization methods that generate and use random variables. For stochastic optimization problems, the objective functions or constraints are random. Stochastic optimization also include methods with random iterates. Some hybrid methods use random iterates to solve stochastic problems, combining both meanings of stochastic optimization. Stochastic optimization methods generalize deterministic methods for deterministic problems. Methods for stochastic functions Partly random input data arise in such areas as real-time estimation and control, simulation-based optimization where Monte Carlo simulations are run as estimates of an actual system, and problems where there is experimental (random) error in the measurements of the criterion. In such cases, knowledge that the function values are contaminated by random "noise" leads naturally to algorithms that use statistical inference tools to estimate the "true" values of the function and/or make sta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Simulated Annealing
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. For large numbers of local optima, SA can find the global optimum. It is often used when the search space is discrete (for example the traveling salesman problem, the boolean satisfiability problem, protein structure prediction, and job-shop scheduling). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to exact algorithms such as gradient descent or branch and bound. The name of the algorithm comes from annealing in metallurgy, a technique involving heating and controlled cooling of a material to alter its physical properties. Both are attributes of the material that depend on their thermodynamic free energy ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Content
In information theory, the information content, self-information, surprisal, or Shannon information is a basic quantity derived from the probability of a particular event occurring from a random variable. It can be thought of as an alternative way of expressing probability, much like odds or log-odds, but which has particular mathematical advantages in the setting of information theory. The Shannon information can be interpreted as quantifying the level of "surprise" of a particular outcome. As it is such a basic quantity, it also appears in several other settings, such as the length of a message needed to transmit the event given an optimal source coding of the random variable. The Shannon information is closely related to ''entropy'', which is the expected value of the self-information of a random variable, quantifying how surprising the random variable is "on average". This is the average amount of self-information an observer would expect to gain about a random variable whe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Algorithm
In 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 via biologically inspired operators such as selection, crossover, and mutation. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference. Methodology Optimization problems In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encod ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Free Will
Free will is generally understood as the capacity or ability of people to (a) choice, choose between different possible courses of Action (philosophy), action, (b) exercise control over their actions in a way that is necessary for moral responsibility, or (c) be the ultimate source or originator of their actions. There are different theories as to its nature, and these aspects are often emphasized differently depending on philosophical tradition, with debates focusing on whether and how such freedom can coexist with determinism, divine foreknowledge, and other constraints. Free will is closely linked to the concepts of moral responsibility, praise, culpability, and other judgements which apply only to actions that are freely chosen. It is also connected with the concepts of Advice (opinion), advice, persuasion, deliberation, and Prohibitionism, prohibition. Traditionally, only actions that are freely Will (philosophy), willed are seen as deserving credit or blame. Whether free ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fisher's Fundamental Theorem Of Natural Selection
Fisher's fundamental theorem of natural selection is an idea about genetic variance in population genetics developed by the statistician and evolutionary biologist Ronald Fisher. The proper way of applying the abstract mathematics of the theorem to actual biology has been a matter of some debate, however, it is a true theorem. It states: :"The rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time." Or in more modern terminology: :"The rate of increase in the mean fitness of any organism, at any time, that is ascribable to natural selection acting through changes in gene frequencies, is exactly equal to its genetic variance in fitness at that time". History The theorem was first formulated in Fisher's 1930 book ''The Genetical Theory of Natural Selection''. Fisher likened it to the law of entropy in physics, stating that "It is not a little instructive that so similar a law should hold the supreme position among the bi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Entropy In Thermodynamics And Information Theory
Because the mathematical expressions for information theory developed by Claude Shannon and Ralph Hartley in the 1940s are similar to the mathematics of statistical thermodynamics worked out by Ludwig Boltzmann and J. Willard Gibbs in the 1870s, in which the concept of entropy is central, Shannon was persuaded to employ the same term 'entropy' for his measure of uncertainty. Information entropy is often presumed to be equivalent to physical (thermodynamic) entropy. Equivalence of form of the defining expressions The defining expression for entropy in the theory of statistical mechanics established by Ludwig Boltzmann and J. Willard Gibbs in the 1870s, is of the form: : S = - k_\text \sum_i p_i \ln p_i , where p_i is the probability of the microstate ''i'' taken from an equilibrium ensemble, and k_B is the Boltzmann constant. The defining expression for entropy in the theory of information established by Claude E. Shannon in 1948 is of the form: : H = - \sum_i p_i \log_b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Efficiency
Efficiency is the often measurable ability to avoid making mistakes or wasting materials, energy, efforts, money, and time while performing a task. In a more general sense, it is the ability to do things well, successfully, and without waste. In more mathematical or scientific terms, it signifies the level of performance that uses the least amount of inputs to achieve the highest amount of output. It often specifically comprises the capability of a specific application of effort to produce a specific outcome with a minimum amount or quantity of waste, expense, or unnecessary effort.Sickles, R., and Zelenyuk, V. (2019).Measurement of Productivity and Efficiency: Theory and Practice. Cambridge: Cambridge University Press. . Efficiency refers to very different inputs and outputs in different fields and industries. In 2019, the European Commission said: "Resource efficiency means using the Earth's limited resources in a sustainable procent manner while minimising impacts on the envi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
