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Evolutionary Computing
Evolutionary computation from computer science is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes as well as, depending on the method, mixing parental information. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection), mutation and possibly recombination. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm. Evolutionary computation techniques can p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Darwin Image Evolution From Random Patches
Darwin may refer to: Common meanings * Charles Darwin (1809–1882), English naturalist and writer, best known as the originator of the theory of biological evolution by natural selection * Darwin, Northern Territory, a capital city in Australia, named after the naturalist Arts and entertainment * ''Darwin'' (1920 film), a German silent film * ''Darwin'' (2011 film), a documentary * ''Darwin'' (2015 film), a science fiction film by Alain Desrochers * Darwin (''seaQuest DSV''), a dolphin in the TV series ''seaQuest DSV'' * '' Darwin!'', a 1972 album by Banco del Mutuo Soccorso * '' Darwin: The Life of a Tormented Evolutionist'', a 1991 biography of Charles Darwin * Darwin (Marvel Comics), a Marvel Comics fictional superhero associated with the X-Men * Darwin Watterson, a character from the 2011 animated TV series ''The Amazing World of Gumball'' Computing * Darwin (ADL), an architecture description language * Darwin (operating system), the Unix base for Apple's iOS and macOS ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fitness Function
A fitness function is a particular type of objective or cost function that is used to summarize, as a single figure of merit, how close a given candidate solution is to achieving the set aims. It is an important component of evolutionary algorithms (EA), such as genetic programming, evolution strategies or genetic algorithms. An EA is a metaheuristic that reproduces the basic principles of biological evolution as a computer algorithm in order to solve challenging optimization or planning tasks, at least approximately. For this purpose, many candidate solutions are generated, which are evaluated using a fitness function in order to guide the evolutionary development towards the desired goal. Similar quality functions are also used in other metaheuristics, such as ant colony optimization or particle swarm optimization. In the field of EAs, each candidate solution, also called an ''individual'', is commonly represented as a string of numbers (referred to as a chromosome). A ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ingo Rechenberg
Ingo Rechenberg (20 November 1934 – 25 September 2021) was a German researcher and professor in the field of bionics. Rechenberg was a pioneer of the fields of evolutionary computation and artificial evolution. In the 1960s and 1970s he invented a highly influential set of optimization methods known as evolution strategies (from German ''Evolutionsstrategie''). His group successfully applied the new algorithms to challenging problems such as aerodynamic wing design. These were the first serious technical applications of artificial evolution, an important subset of the still growing field of bionics. Rechenberg was born in Berlin. He was educated at Technische Universität Berlin and at the University of Cambridge The University of Cambridge is a Public university, public collegiate university, collegiate research university in Cambridge, England. Founded in 1209, the University of Cambridge is the List of oldest universities in continuous operation, wo .... Since 1972 h ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Finite-state Machine
A finite-state machine (FSM) or finite-state automaton (FSA, plural: ''automata''), finite automaton, or simply a state machine, is a mathematical model of computation. It is an abstract machine that can be in exactly one of a finite number of ''State (computer science), states'' at any given time. The FSM can change from one state to another in response to some Input (computer science), inputs; the change from one state to another is called a ''transition''. An FSM is defined by a list of its states, its initial state, and the inputs that trigger each transition. Finite-state machines are of two types—Deterministic finite automaton, deterministic finite-state machines and Nondeterministic finite automaton, non-deterministic finite-state machines. For any non-deterministic finite-state machine, an equivalent deterministic one can be constructed. The behavior of state machines can be observed in many devices in modern society that perform a predetermined sequence of actions d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evolutionary Programming
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover. Evolutionary programming differs from evolution strategy ES(\mu+\lambda) in one detail. All individuals are selected for the new population, while in ES(\mu+\lambda), every individual has the same probability to be selected. It is one of the four major evolutionary algorithm paradigms. History It was first used by Lawrence J. Fogel in the US in 1960 in order to use simulated evolution as a learning process aiming to generate artificial intelligence. It was used to evolve finite-state machines as predictors. See also * Artificial intelligence * 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 g ... * Genetic opera ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lawrence J
Lawrence may refer to: Education Colleges and universities * Lawrence Technological University, a university in Southfield, Michigan, United States * Lawrence University, a liberal arts university in Appleton, Wisconsin, United States Preparatory & high schools * Lawrence Academy at Groton, a preparatory school in Groton, Massachusetts, United States * Lawrence College, Ghora Gali, a high school in Pakistan * Lawrence School, Lovedale, a high school in India * The Lawrence School, Sanawar, a high school in India Research laboratories * Lawrence Berkeley National Laboratory, United States * Lawrence Livermore National Laboratory, United States People * Lawrence (given name), including a list of people with the name * Lawrence (surname), including a list of people with the name * Lawrence (band), an American soul-pop group * Lawrence (judge royal) (died after 1180), Hungarian nobleman, Judge royal 1164–1172 * Lawrence (musician), Lawrence Hayward (born 1961), British m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Programming
Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs. It applies the genetic operators selection (evolutionary algorithm), selection according to a predefined fitness function, fitness measure, mutation (evolutionary algorithm), mutation and crossover (evolutionary algorithm), crossover. The crossover operation involves swapping specified parts of selected pairs (parents) to produce new and different offspring that become part of the new generation of programs. Some programs not selected for reproduction are copied from the current generation to the new generation. Mutation involves substitution of some random part of a program with some other random part of a program. Then the selection and other operations are recursively applied to the new generation of programs. Typically, members of each new generation are on average more fit than the members of the previous gene ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evolutionary Programming
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover. Evolutionary programming differs from evolution strategy ES(\mu+\lambda) in one detail. All individuals are selected for the new population, while in ES(\mu+\lambda), every individual has the same probability to be selected. It is one of the four major evolutionary algorithm paradigms. History It was first used by Lawrence J. Fogel in the US in 1960 in order to use simulated evolution as a learning process aiming to generate artificial intelligence. It was used to evolve finite-state machines as predictors. See also * Artificial intelligence * 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 g ... * Genetic opera ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evolution Strategy
Evolution strategy (ES) from computer science is a subclass of evolutionary algorithms, which serves as an optimization (mathematics), optimization technique. It uses the major genetic operators mutation (evolutionary algorithm), mutation, recombination (evolutionary algorithm), recombination and selection (evolutionary algorithm), selection of parents. History The 'evolution strategy' optimization technique was created in the early 1960s and developed further in the 1970s and later by Ingo Rechenberg, Hans-Paul Schwefel and their co-workers. Methods Evolution strategies use natural problem-dependent representations, so Genetic representation, problem space and Genetic representation, search space are identical. In common with evolutionary algorithms, the operators are applied in a loop. An iteration of the loop is called a generation. The sequence of generations is continued until a termination criterion is met. The special feature of the ES is the self-adaptation of mutation ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reinforcement Learning
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dyn ... [...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] |
Neural Network
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or signal pathways. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural networks. *In neuroscience, a '' biological neural network'' is a physical structure found in brains and complex nervous systems – a population of nerve cells connected by synapses. *In machine learning, an '' artificial neural network'' is a mathematical model used to approximate nonlinear functions. Artificial neural networks are used to solve artificial intelligence problems. In biology In the context of biology, a neural network is a population of biological neurons chemically connected to each other by synapses. A given neuron can be connected to hundreds of thousands of synapses. Each neuron sends and receives electrochemical signals called action potentials to its conne ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
