In computer science and operations research, a genetic algorithm is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms. Genetic algorithms are used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation and selection. John Holland introduced genetic algorithms in 1960 based on the concept of Darwin’s theory of evolution. In a genetic algorithm, a population of candidate solutions to an optimization problem is evolved toward better solutions; each candidate solution has a set of properties which can be altered. The evolution starts from a population of randomly generated individuals, is an iterative process, with the population in each iteration called a generation. In each generation, the fitness of every individual in the population is evaluated; the more fit individuals are stochastically selected from the current population, each individual's genome is modified to form a new generation.
The new generation of candidate solutions is used in the next iteration of the algorithm. The algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. A typical genetic algorithm requires: a genetic representation of the solution domain, a fitness function to evaluate the solution domain. A standard representation of each candidate solution is as an array of bits. Arrays of other types and structures can be used in the same way; the main property that makes these genetic representations convenient is that their parts are aligned due to their fixed size, which facilitates simple crossover operations. Variable length representations may be used, but crossover implementation is more complex in this case. Tree-like representations are explored in genetic programming and graph-form representations are explored in evolutionary programming. Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and to improve it through repetitive application of the mutation, crossover and selection operators.
The population size depends on the nature of the problem, but contains several hundreds or thousands of possible solutions. The initial population is generated randomly, allowing the entire range of possible solutions; the solutions may be "seeded" in areas where optimal solutions are to be found. During each successive generation, a portion of the existing population is selected to breed a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions are more to be selected. Certain selection methods rate the fitness of each solution and preferentially select the best solutions. Other methods rate only a random sample of the population, as the former process may be time-consuming; the fitness function is defined over the genetic representation and measures the quality of the represented solution. The fitness function is always problem dependent. For instance, in the knapsack problem one wants to maximize the total value of objects that can be put in a knapsack of some fixed capacity.
A representation of a solution might be an array of bits, where each bit represents a different object, the value of the bit represents whether or not the object is in the knapsack. Not every such representation is valid, as the size of objects may exceed the capacity of the knapsack; the fitness of the solution is the sum of values of all objects in the knapsack if the representation is valid, or 0 otherwise. In some problems, it is hard or impossible to define the fitness expression; the next step is to generate a second generation population of solutions from those selected through a combination of genetic operators: crossover, mutation. For each new solution to be produced, a pair of "parent" solutions is selected for breeding from the pool selected previously. By producing a "child" solution using the above methods of crossover and mutation, a new solution is created which shares many of the characteristics of its "parents". New parents are selected for each new child, the process continues until a new population of solutions of appropriate size is generated.
Although reproduction methods that are based on the use of two parents are more "biology inspired", some research suggests that more than two "parents" generate higher quality chromosomes. These processes result in the next generation population of chromosomes, different from the initial generation; the average fitness will have increased by this procedure for the population, since only the best organisms from the first generation are selected for breeding, along with a small proportion of less fit solutions. These less fit solutions ensure genetic diversity within the genetic po
Alan Mathison Turing was an English mathematician, computer scientist, cryptanalyst and theoretical biologist. Turing was influential in the development of theoretical computer science, providing a formalisation of the concepts of algorithm and computation with the Turing machine, which can be considered a model of a general-purpose computer. Turing is considered to be the father of theoretical computer science and artificial intelligence. Despite these accomplishments, he was never recognised in his home country during his lifetime, due to his homosexuality, a crime in the UK. During the Second World War, Turing worked for the Government Code and Cypher School at Bletchley Park, Britain's codebreaking centre that produced Ultra intelligence. For a time he led Hut 8, the section, responsible for German naval cryptanalysis. Here, he devised a number of techniques for speeding the breaking of German ciphers, including improvements to the pre-war Polish bombe method, an electromechanical machine that could find settings for the Enigma machine.
Turing played a pivotal role in cracking intercepted coded messages that enabled the Allies to defeat the Nazis in many crucial engagements, including the Battle of the Atlantic, in so doing helped win the war. Counterfactual history is difficult with respect to the effect Ultra intelligence had on the length of the war, but at the upper end it has been estimated that this work shortened the war in Europe by more than two years and saved over 14 million lives. After the war, Turing worked at the National Physical Laboratory, where he designed the Automatic Computing Engine, one of the first designs for a stored-program computer. In 1948, Turing joined Max Newman's Computing Machine Laboratory at the Victoria University of Manchester, where he helped develop the Manchester computers and became interested in mathematical biology, he wrote a paper on the chemical basis of morphogenesis and predicted oscillating chemical reactions such as the Belousov–Zhabotinsky reaction, first observed in the 1960s.
Turing was prosecuted in 1952 for homosexual acts. He accepted chemical castration treatment, as an alternative to prison. Turing died 16 days before his 42nd birthday, from cyanide poisoning. An inquest determined his death as a suicide, but it has been noted that the known evidence is consistent with accidental poisoning. In 2009, following an Internet campaign, British Prime Minister Gordon Brown made an official public apology on behalf of the British government for "the appalling way he was treated". Queen Elizabeth II granted Turing a posthumous pardon in 2013; the Alan Turing law is now an informal term for a 2017 law in the United Kingdom that retroactively pardoned men cautioned or convicted under historical legislation that outlawed homosexual acts. Turing was born in Maida Vale, while his father, Julius Mathison Turing, was on leave from his position with the Indian Civil Service at Chatrapur in the Madras Presidency and presently in Odisha state, in India. Turing's father was the son of a clergyman, the Rev. John Robert Turing, from a Scottish family of merchants, based in the Netherlands and included a baronet.
Turing's mother, Julius' wife, was Ethel Sara Turing, daughter of Edward Waller Stoney, chief engineer of the Madras Railways. The Stoneys were a Protestant Anglo-Irish gentry family from both County Tipperary and County Longford, while Ethel herself had spent much of her childhood in County Clare. Julius' work with the ICS brought the family to British India, where his grandfather had been a general in the Bengal Army. However, both Julius and Ethel wanted their children to be brought up in Britain, so they moved to Maida Vale, where Alan Turing was born on 23 June 1912, as recorded by a blue plaque on the outside of the house of his birth the Colonnade Hotel. Turing had John. Turing's father's civil service commission was still active and during Turing's childhood years Turing's parents travelled between Hastings in England and India, leaving their two sons to stay with a retired Army couple. At Hastings, Turing stayed at Baston Lodge, Upper Maze Hill, St Leonards-on-Sea, now marked with a blue plaque.
The plaque was unveiled on 23 June 2012, the centenary of Turing's birth. Early in life, Turing showed signs of the genius that he was to display prominently, his parents purchased a house in Guildford in 1927, Turing lived there during school holidays. The location is marked with a blue plaque. Turing's parents enrolled him at St Michael's, a day school at 20 Charles Road, St Leonards-on-Sea, at the age of six; the headmistress recognised his talent early on. Between January 1922 and 1926, Turing was educated at Hazelhurst Preparatory School, an independent school in the village of Frant in Sussex. In 1926, at the age of 13, he went on to Sherborne School, a boarding independent school in the market town of Sherborne in Dorset; the first day of term coincided with the 1926 General Strike in Britain, but he was so determined to attend, that he rode his bicycle unaccompanied 60 miles from Southampton to Sherborne, stopping overnight at an inn. Turing's natural inclination towards mathematics and science did not earn him respect from some of the teachers at Sherborne, whose definition of education placed more emphasis on the classics.
His headmaster wrote to his parents: "I hope. If he is to stay at public school
The cerebral cortex known as the cerebral mantle, is the outer layer of neural tissue of the cerebrum of the brain, in humans and other mammals. It is separated into two cortices, by the longitudinal fissure that divides the cerebrum into the left and right cerebral hemispheres; the two hemispheres are joined beneath the cortex by the corpus callosum. The cerebral cortex is the largest site of neural integration in the central nervous system, it plays a key role in memory, perception, thought and consciousness. In most mammals, apart from small mammals that have small brains, the cerebral cortex is folded, providing a greater surface area in the confined volume of the cranium. Apart from minimising brain and cranial volume cortical folding is crucial for the wiring of the brain and its functional organisation. In mammals with a small brain there is no folding and the cortex is smooth. A fold or ridge in the cortex is termed a gyrus and a groove is termed a sulcus; these surface convolutions appear during fetal development and continue to mature after birth through the process of gyrification.
In the human brain the majority of the cerebral cortex is not visible from the outside, but buried in the sulci, the insular cortex is hidden. The major sulci and gyri mark the divisions of the cerebrum into the lobes of the brain. There are between 16 billion neurons in the cerebral cortex; these are organised into cortical columns and minicolumns of neurons that make up the layers of the cortex. Most of the cerebral cortex consists of the six-layered neocortex. Cortical areas have specific functions; the cerebral cortex is the outer covering of the surfaces of the cerebral hemispheres and is folded into peaks called gyri, grooves called sulci. In the human brain it is between two and three or four millimetres thick, makes up 40 per cent of the brain's mass. There are between 14 and 16 billion neurons in the cortex, these are organized in cortical columns, minicolumns of the layers of the cortex. About two thirds of the cortical surface is buried in the sulci and the insular cortex is hidden; the cortex is thickest over thinnest at the bottom of a sulcus.
The cerebral cortex is folded in a way that allows a large surface area of neural tissue to fit within the confines of the neurocranium. When unfolded in the human, each hemispheric cortex has a total surface area of about 1.3 square feet. The folding is inward away from the surface of the brain, is present on the medial surface of each hemisphere within the longitudinal fissure. Most mammals have a cerebral cortex, convoluted with the peaks known as gyri and the troughs or grooves known as sulci; some small mammals including some small rodents have smooth cerebral surfaces without gyrification. The larger sulci and gyri mark the divisions of the cortex of the cerebrum into the lobes of the brain. There are four main lobes: the frontal lobe, parietal lobe, temporal lobe, occipital lobe; the insular cortex is included as the insular lobe. The limbic lobe is a rim of cortex on the medial side of each hemisphere and is often included. There are three lobules of the brain described: the paracentral lobule, the superior parietal lobule, the inferior parietal lobule.
For species of mammals, larger brains tend to have thicker cortices. The smallest mammals, such as shrews, have a neocortical thickness of about 0.5 mm. There is an logarithmic relationship between brain weight and cortical thickness. Magnetic resonance imaging of the brain makes it possible to get a measure for the thickness of the human cerebral cortex and relate it to other measures; the thickness of different cortical areas varies but in general, sensory cortex is thinner than motor cortex. One study has found some positive association between the cortical intelligence. Another study has found that the somatosensory cortex is thicker in migraine sufferers, though it is not known if this is the result of migraine attacks or the cause of them. A study using a larger patient population reports no change in the cortical thickness in migraine sufferers. A genetic disorder of the cerebral cortex, whereby decreased folding in certain areas results in a microgyrus, where there are four layers instead of six, is in some instances seen to be related to dyslexia.
The six cortical layers of the neocortex each contain a characteristic distribution of different neurons and their connections with other cortical and subcortical regions. There are direct connections between different cortical areas and indirect connections via the thalamus. One of the clearest examples of cortical layering is the line of Gennari in the primary visual cortex; this is a band of whiter tissue that can be observed with the naked eye in the fundus of the calcarine sulcus of the occipital lobe. The line of Gennari is composed of axons bringing visual information from the thalamus into layer IV of the visual cortex. Staining cross-sections of the cortex to reveal the position of neuronal cell bodies and the intracortical axon tracts allowed neuroanatomists in the early 20th century to produce a detailed description of the laminar structure of the cortex in different species. After the work of Korbinian Brodmann the neurons of the cerebral cortex are grouped into six main layers, from the outer pial surface to the inner white matter.
Layer I is the molecular layer, contains few scattered neurons, including GABAergic rosehip neurons. Layer I consists of extensions of apical dendritic tufts of pyramidal neurons and horiz
Automatic Computing Engine
The Automatic Computing Engine was a British early electronic stored-program computer designed by Alan Turing. The project was managed by John R. Womersley, superintendent of the Mathematics Division of the National Physical Laboratory; the use of the word Engine was in homage to Charles Babbage and his Difference Engine and Analytical Engine. Turing's technical design Proposed Electronic Calculator was the product of his theoretical work in 1936 "On Computable Numbers" and his wartime experience at Bletchley Park where the Colossus computers had been successful in breaking German military codes. In his 1936 paper, Turing described his idea as a "universal computing machine", but it is now known as the Universal Turing machine. On 19 February 1946 Turing presented a detailed paper to the National Physical Laboratory Executive Committee, giving the first reasonably complete design of a stored-program computer. However, because of the strict and long-lasting secrecy around the Bletchley Park work, he was prohibited from explaining that he knew that his ideas could be implemented in an electronic device.
The better-known EDVAC design presented in the First Draft of a Report on the EDVAC, by John von Neumann, who knew of Turing's theoretical work, received much publicity, despite its incomplete nature and questionable lack of attribution of the sources of some of the ideas. Turing's report on the ACE was written in late 1945 and included detailed logical circuit diagrams and a cost estimate of £11,200, he felt that speed and size of memory were crucial and he proposed a high-speed memory of what would today be called 25 kilobytes, accessed at a speed of 1 MHz. The ACE implemented subroutine calls, whereas the EDVAC did not, what set the ACE apart from the EDVAC was the use of Abbreviated Computer Instructions, an early form of programming language, it was planned that Tommy Flowers, the engineer at the Post Office Research Station at Dollis Hill in north London, responsible for building the Colossus computers should build the ACE, but because of the secrecy around his wartime achievements and the pressure of post-war work, this was not possible.
Turing's colleagues at the NPL, not knowing about Colossus, thought that the engineering work to build a complete ACE was too ambitious, so the first version of the ACE, built was the Pilot Model ACE, a smaller version of Turing's original design. The Pilot ACE had 1450 thermionic valves, used mercury delay lines for its main memory; each of the 12 delay lines could store 32 instructions or data words of 32 bits. This ran its first program on 10 May 1950, at which time it was the fastest computer in the world with a clock speed of 1 MHz; the first production versions of the Pilot ACE, the English Electric DEUCE, of which 31 were sold, were delivered in 1955. A second implementation of the ACE design was the MOSAIC; this was built by Allen Coombs and William Chandler of Dollis Hill who had worked with Tommy Flowers on building the ten Colossus computers. It was installed at the Telecommunications Research Establishment which soon became the Royal Radar Establishment at Malvern and ran its first program in late 1952 or early 1953.
It was used to calculate aircraft trajectories from radar data. It continued operating until the early 1960s; the principles of the ACE design were used in the Bendix Corporation's G-15 computer. The engineering design was done by Harry Huskey who had spent 1947 in the ACE section at the NPL, he contributed to the hardware designs for the EDVAC. The first G-15 ran in 1954 and, as a small single-user machine, some consider it to be the first personal computer. Other derivatives of the ACE include the EMI Electronic Business Machine and the Packard Bell PB250. Carpenter, B. E.. "The other Turing machine", The Computer Journal, 20: 269–279, doi:10.1093/comjnl/20.3.269 Carpenter, B. E.. Jack, Colossus: The Secrets of Bletchley Park's Codebreaking Computers, Oxford: Oxford University Press, pp. 108–110, ISBN 978-0-19-284055-4 Lavington, Simon H. Early British Computers: The Story of Vintage Computers and The People Who Built Them, Manchester University Press Wilkinson, J. H. "Turing's Work at the National Physical Laboratory and the Construction of Pilot ACE, DEUCE and ACE", in Metropolis, Nicholas.
A History of Computing in the Twentieth Century, New York: Academic Press Yates, David M. Turing's Legacy: A History of Computing at the National Physical Laboratory, 1945-1995, London: Science Museum Oral history interview with Donald W. Davies, Charles Babbage Institute, University of Minnesota. Davies describes computer projects at the U. K. National Physical Laboratory, from the 1947 design work of Alan Turing to the development of the two ACE computers. Davies discusses a much larger, second ACE, the decision to contract with English Electric Company to build the DEUCE—possibly the first commercially produced computer in Great Britain. Events in the history of NPL — ACE computer
In digital electronics, a NAND gate is a logic gate which produces an output, false only if all its inputs are true. A LOW output results only if all the inputs to the gate are HIGH. A NAND gate is made using transistors and junction diodes. By De Morgan's theorem, a two-input NAND gate's logic may be expressed as AB=A+B, making a NAND gate equivalent to inverters followed by an OR gate; the NAND gate is significant because any boolean function can be implemented by using a combination of NAND gates. This property is called functional completeness, it shares this property with the NOR gate. Digital systems employing certain logic circuits take advantage of NAND's functional completeness; the function NAND is logically equivalent to NOT. One way of expressing A NAND B is A ∧ B ¯, where the symbol ∧ signifies AND and the bar signifies the negation of the expression under it: in essence ¬. There are three symbols for NAND gates: the MIL/ANSI symbol, the IEC symbol and the deprecated DIN symbol sometimes found on old schematics.
For more information see logic gate symbols. The ANSI symbol for the NAND gate is a standard AND gate with an inversion bubble connected. NAND gates are basic logic gates, as such they are recognised in TTL and CMOS ICs; the NAND gate has the property of functional completeness. That is, any other logic function can be implemented using only NAND gates. An entire processor can be created using NAND gates alone. In TTL ICs using multiple-emitter transistors, it requires fewer transistors than a NOR gate. If no specific NAND gates are available, one can be made from NOR gates, because NAND and NOR gates are considered the "universal gates", meaning that they can be used to make all the other gates. AND gate OR gate NOT gate NOR gate XOR gate XNOR gate Boolean algebra Logic gate NAND logic Digital electronics Flash memory TTL NAND and AND gates – All About Circuits