Aarushhh/wikipedia-simple-splitted huggingface dataset:
Azad Kashmir Day celebrates the 67th day of the Azad Jammu Kashmir government, created on 24 October 1947. However, most people celebrate it on 26 October as they class it as a lucky day once all the papers were signed and settled.

Kashmir
Holidays of Pakistan
In cryptography, a stream cipher is a symmetric key cipher where plaintext bits are combined with a pseudorandom cipher bit stream (keystream) using an exclusive-or (xor) operation. In a stream cipher the Plaintext digits are encrypted one at a time, and the transformation of successive digits varies during the encryption state. An alternative name is a state cipher, as the encryption of each digit is dependent on the current state. In practice, the digits are typically single bits or bytes.

Stream ciphers represent a different approach to symmetric encryption from block ciphers. Block ciphers operate on large blocks of fixed length. Stream ciphers typically execute at a higher speed than block ciphers and have lower hardware requirements. However, stream ciphers can be susceptible to serious security problems if used incorrectly; for example, in particular, the same starting state must never be used twice.
A stream cipher makes use of a much smaller and more convenient cryptographic key, for example 128 bits keys. Based on this key, it generates a pseudorandom keystream which can be combined with the plaintext digits in a similar way to the one-time pad encryption algorithm. However,  because the keystream is pseudorandom, and not truly random, the security associated with the one-time pad cannot be applied and it is quite possible for a stream cipher to be completely insecure.

Types of stream ciphers
A stream cipher generates successive elements of the keystream based on an internal state. This state is updated in two ways: 
 If the state changes independently of the plaintext or ciphertext messages, the cipher is classified as a synchronous stream cipher.
 If the state is updated based on previous changes of the ciphertext digits, the cipher is classified as a self-synchronising stream ciphers.
Synchronous stream ciphers
In a synchronous stream cipher a stream of pseudo-random digits is generated independently of the plaintext and ciphertext messages, and then combined with the plaintext (to encrypt) or with the ciphertext (to decrypt). In the most common form, binary digits(bits) are used, and the keystream is combined with the plaintext using the exclusive or operation (XOR). This is termed a binary additive stream cipher.

In a synchronous stream cipher, the sender and receiver must be in  synchronous for decryption to be successful. If digits are added or removed from the message during transmission, synchronisation is lost. To restore synchronisation, various offsets can be tried systematically to obtain the correct decryption. Another approach is to mark the ciphertext with markers at regular points in the output.
Self-synchronizing stream ciphers
Self-synchronizing stream ciphers is another technique that uses part of the previous N ciphertext digits to compute the keystream. Such schemes are known also as asynchronous stream ciphers or ciphertext autokey (CTAK). The idea of self-synchronization was patented in 1946, and has the advantage that the receiver will automatically synchronise with the keystream generator after receiving N ciphertext digits, making it easier to recover if digits are dropped or added to the message stream. Single-digit errors are limited in their effect, affecting only up to N plaintext digits. It is somewhat more difficult to perform active attacks on self-synchronising stream ciphers rather than synchronous counterparts.

An example of a self-synchronising stream cipher is a block cipher in cipher-feedback mode (CFB).
Linear feedback shift register-based stream ciphers
Binary stream ciphers are often constructed using linear feedback shift registers (LFSRs) because they can be easily implemented in hardware and can be quickly analysed mathematically. However, the use of LFSRs only is insufficient to provide good security. Various schemes have been designed to increase the security of LFSRs.

Non-linear combining functions
Because LFSRs are inherently linear, one technique for removing the linearity is to feed the outputs of a group of parallel LFSRs into a non-linear Boolean function to form a combination generator. Various properties of such a combining function are important for ensuring the security of the resultant scheme, for example, in order to avoid correlation attacks.
Clock-controlled generators
Normally LFSRs are stepped regularly. One technique to introducting non-linearity is to have the LFSR clocked irregularly, controlled by the output of a second LFSR. Such generators include the stop-and-go generator, the alternating step generator and the shrinking generator.

The stop-and-go generator (Beth and Piper, 1984) consists of two LFSRs. One LFSR is clocked if the output of a second is a "1", otherwise it repeats its previous output. This output is then (in some versions) combined with the output of a third LFSR clocked at a regular rate.

The shrinking generator uses a different technique. Two LFSRs are used, both clocked regularly in the following way:
If the output of the first LFSR is "1", the output of the second LFSR becomes the output of the generator.
If the output of the first LFSR "0", the output of the second is discarded, and no bit is output by the generator.
This technique suffers from timing attacks on the second generator, since the speed of the output is variable in a manner that depends on the second generator's state.  This can be improved by buffering the output.

Filter generator
Another approach to improving the security of an LFSR is to pass the entire state of a single LFSR into a non-linear filtering function.

Other designs
Instead of a linear driving device, one may use a nonlinear update function. For example, Klimov and Shamir proposed triangular functions (T-Functions) with a single cycle on n bit words.

Security
To be secure, the period of the keystream (the number of digits output before the stream repeats itself)  needs to be large enough. If the sequence repeats, then the overlapping ciphertexts can be aligned against each other "in depth", and there are techniques which allow extraction of the plaintext form ciphertexts generated using these methods.

Usage
Usage

Stream ciphers are often used in applications where plaintext comes in quantities of unknowable length as in secure wireless connections. If a block cipher were to be used in this type of application, the designer would need to choose either transmission efficiency or implementation complexity, since block ciphers cannot directly work on blocks shorter than their block size. For example, if a 128-bit block cipher received separate 32-bit bursts of plaintext, three quarters of the transmitted data need padding. Block ciphers must be used in ciphertext stealing or residual block termination mode to avoid padding, while stream ciphers eliminate this issue by operating on the smallest transmitted unit (usually bytes).
Another advantage of stream ciphers in military cryptography is that the cipher stream can be generated by an encryption device that is subject to strict security measures then fed to other devices, e.g. a radio set, which will perform the xor operation as part of their function.  The other device can be designed for used in less securely environments.

RC4 is the most widely used stream cipher in software; others include: 
A5/1,
A5/2,
Chameleon, 
FISH, 
Helix,
ISAAC, 
MUGI,
Panama,
Phelix, 
Pike,
SEAL, 
SOBER,
SOBER-128 and
WAKE.

Comparison Of Stream Ciphers

Related pages
 eSTREAM

References
 Matt J. B. Robshaw, Stream Ciphers Technical Report TR-701, version 2.0, RSA Laboratories, 1995 (PDF) .
 Thomas Beth and Fred Piper, The Stop-and-Go Generator. EUROCRYPT 1984, pp 88–92.

Other websites
 RSA technical report on stream cipher operation. 
 Analysis of Lightweight Stream Ciphers (thesis by S. Fischer). 

Cryptography
Nyon is a municipality in the district of Nyon in the canton of Vaud in Switzerland. It is found 25 kilometers north of Geneva, and is part of the Geneva metropolitan area. The town has (as of 2006) 17,267 people.

History 
Nyon was built by the Romans between 50 BCE and 44 BCE, and was called "Colonia Iulia Equestris" or "Colonia Equestris Noiodunum". It grew to be one of the most important Roman colonies in modern-day Switzerland, with a forum, a basilica and an amphitheater that was discovered in 1996.

Modern Day Nyon 
Today, Nyon has a high school (Gymnase de Nyon), a hospital, a movie theatre, hotels, restaurants, cafes and a popular tourist attraction - the castle. 
In the last week of July each year, Nyon hosts the Paléo Festival - a huge music festival.

Other websites
Other websites 

Official website of the town (available in French, English and German)
Web site of the Roman Museum of Nyon (same languages as above link)
Paléo Festival
Rugby Club Nyon
Ultimate Frisbee Club, Les Mange-Disques de Nyon
La Redoute, Popular neighbourhood of Nyon

Municipalities in Nyon
Cities in Switzerland
Letterkenny () is a town in County Donegal of the Ireland. It is  on the River Swilly and has a population of 17,586.

In 1824, when the first description of Letterkenny as a modern town was written, it was stated that: "Within half a mile is the Port of Ballyraine, whither vessels of 100 tons bring iron, salt and colonial produce and whence they export hides and butter". Nothing is left of the port now except the warehouses.

Letterkenny achieved town status in the early 1920s following the partition of Ireland.

References

Towns in Ireland
County Donegal
Thalwil is a municipality in the district of Horgen in the canton of Zürich in Switzerland.

History
Early Middle Ages Thalwil was Alemannic farmland.

Thalwil was originally four Wachten, or hamlets:
 Ludretikon, the oldest hamlet (mentioned in 915), bordering Rüschlikon
 Oberdorf (Upper Village), the area around the church
 Unterdorf (Lower Village), the area roughly south of the railway station
 Langnau am Albis, (which separated from Thalwil around 1713)

In the Middle Ages, most income was made in farming, vineyards and a small amount of fishing on the lake. 
Thalwil also has the oldest wood corporation in the Canton of Zurich, the Bannegg-Waldung. It was first mentioned in 1483.

Modern day Thalwil
Today, the economy of Thalwil is mostly some small and medium-sized businesses, and a wide range of shops. Most of the shops are located around Gotthardstrasse- the town centre.
The main source of Thalwil's wealth lies with its 7,000 commuters. These people live in Thalwil, but work in Zurich city. Today, Thalwil has left most of its farming past behind in exchange for a town of commuters, with its public transportation links, low tax and good quality of life.

Other websites 

Official website of the Thalwil Gemeinde 

Blog about life in Thalwil 
Thalwiler Anzeiger newspaper 

Municipalities in Horgen
Cities in Switzerland
A hearth (har-th) is a brick or stone lined fireplace or oven that is used for cooking and heating. Historically, the hearth was the center of the home, as it provided heat for the house. Its Latin name is focus. That is why "focus" is used for many kinds of central point.

In more modern times, the hearth may be section of the fireplace that can be seen. This often includes tiles around the base of the fireplace and the floor.

References

Home
A Jack-o'-lantern is a carved pumpkin, turnip, beet, or potato. In the 19th and 20th centuries people in Scotland and Ireland carved Halloween lanterns from large yellow turnips. This custom has become less prevalent in recent years as supermarkets have begun selling the larger and easier-to-carve American pumpkins. 

In Irish lore, the Jack-o'-lantern is from the story of Stingy Jack. Jack was a clever man who cheated the devil several times. Once when Jack was leaving hell, the devil threw him a glowing coal to light his way. Jack put the coal in a hollowed-out turnip to make the first "Jack-o'-lantern". Lore says Jack can still be seen wandering the world with his turnip lantern, especially around All Saints Day, November 1.

In the United States, the carved pumpkin was first associated with the harvest season in general, long before it became an emblem of Halloween. In 1900, an article on Thanksgiving entertaining recommended a lit Jack-o'-lantern as part of the festivities.
To make a Jack-o'-lantern, the top of the pumpkin surrounding the stump of the vine is cut off. This top will be used as a "lid" when the Jack-o'-lantern is completed. The seeds and pulp inside the pumpkin are scooped out using a spoon or one's hands. The seeds may be roasted in a hot oven, and then salted to make a snack.

Once the inside of the pumpkin is clean, a face is carved into one side of the pumpkin. The eyes and nose are usually triangles. The mouth is usually filled with jagged teeth. A candle, devotional candle in a candle-holder, or a flashlight is put inside the Jack-o'-lantern and lit. The "lid" is replaced. The lid may be placed slightly askew to keep the candle alit.
In the United States, a completely carved and lit Jack-o'-lantern is put on the porch, the porch steps, or in a window to welcome trick-or-treaters on Halloween. Once the pumpkin is carved, it is good for nothing else and lasts only a day or two before it starts to rot. Pumpkins used to make Jack-o'-lanterns are inedible. In the United States, mischief-makers steal the Jack-o'-lanterns of their neighbors. The pumpkins are smashed in the street, dropped from a bridge, or otherwise destroyed.

References

  

Cucurbitaceae
The lakes of Titan, a moon of Saturn, are made of liquid methane. The lakes were found by the Cassini-Huygens space probe. The larger lakes are known as maria (seas) and the smaller ones as lacūs (lakes).

It was possible that there were seas of liquid methane on Titan and first suggested after reading Voyager 1 and Voyager 2 information that showed Titan to have a thick atmosphere, capable of supporting lakes, but exact evidence was not found until 1995 when pictures from the Hubble telescope and other telescopes had already shown some proof of liquid methane possibly in lakes or planet-wide oceans, similar to oceans on Earth.

The Cassini mission proved there are liquid lakes on Titan, but not at first. When the probe arrived in the Saturnian system in 2004, scientists hoped that hydrocarbon lakes or oceans might be seen in reflected sunlight from the surface, but no reflections were found.
Scientists still believed that liquid ethane and methane would be found near Titan's poles, where they expected to find lots of liquid. At Titan's south pole, a large dark area named Ontario Lacus was the first lake identified, possibly created by rain as clouds are often seen in the area. A possible shoreline was also identified at the pole in radar images. As the Huygens-Cassini probe flew by on July 22, 2006, its radar took pictures of the northern part of the planet (which was in its winter), a number of large, smooth areas were found on the surface near the northern pole. Based on this data, scientists announced "definitive evidence of lakes filled with methane on Saturn's moon Titan" in January 2007. The Cassini–Huygens team decided that the smooth areas are the hydrocarbon lakes they had been searching for, the first lakes found off Earth. Some valleys appear to have rivers.
The satellite was not able to show pictures of lakes moving in the wind. The heavy moisture and clouds of methane near Titan’s surface could be a sign of evaporation from lakes. The lakes cover 0.002–0.02% of Titan's surface.

The Cassini-Huygens probe flew by in February, 2007, taking radar and camera pictures of some large areas near the north pole that may be large areas of liquid methane and/or ethane, including one sea larger than 100,000 km² (larger than Lake Superior), and another area similar in size to the Caspian Sea. Pictures taken near Titan's southern pole in October 2007 showed similar, but far smaller, areas that look like lakes.
[[Image:Huygens shoreline.jpg|thumb|Picture of Titan taken during Huygens''' landing, showing hills, rivers and beaches.]]
The Cassini-Huygens probe, flew very close to Titan in December 2007, and took infrared pictures of the chemicals on the surface and found a lake called Ontario Lacus near the south pole. Scientists studying the infrared pictures decided that one or more of the large lakes in the pictures of Saturn's moon Titan does hold liquid and that the liquid is made of hydrocarbons. They have also found ethane on Titan.  This mission proves Titan to be the only other planet known to have liquid on its surface. The lakes make Titan a very important planet for studying weather science. Liquids, gases and temperatures are at play on Titan.  This should help the science of weather forecasting.
Lakes were not found near near Titan's center of Titan which was studied on January 14, 2005. A part of the Huygens-Cassini probe landed there. The pictures taken by the probe as it fell showed no open areas of liquid, but showed rivers that had dried. It was at first thought that dark areas near the center of Titan could be a lake of a liquid or tar-like substance, but the probe landed on the dark area, and it is solid without any liquid. A device known as a penetrometer studied the surface as the probe landed and seemed to find wet clay. Scientists believe that the penetrometer may have landed on a large pebble, and they say that the surface is similar to sand made of ice. Pictures taken after the probe landed show a  large flat area covered in pebbles. The pebbles may be made of water ice and are partly rounded, which indicates fluids.
On February 13, 2008, scientists said that Titans polar lakes contain "hundreds of times more natural gas and other hydrocarbons than all the known natural gas on Earth." The desert near the center of the planet are not liquid but do have more organics than Earth. In June 2008, Cassini's Visible and Infrared Mapping Spectrometer confirmed the presence of liquid ethane beyond doubt in a lake in Titan's southern hemisphere.

Predictions of Titan's clouds are that during in a Saturnian year, liquid moves from the equator to the poles, in clouds, and falls as rain.

Names of Titans lakes and seas
Titanian maria (large hydrocarbon seas) are named after sea monsters of Earth.

Areas named Lacus'' are believed to be methane lakes. They are named after lakes on Earth.

Related pages
Solar system

References

Saturn's moons
The West Coast blues is a type of blues music related to jazz and jump blues, with lots of piano sounds and jazz guitar solos, which originated from Texas blues musicians that moved to California in the 1940s.  West Coast blues also uses smooth, honey-toned vocals, often sounding similar to urban blues.

Texas and the West Coast
Artist/Musicians

Footnotes

Other websites
Bay Area Blues Society 

Blues music
Third person (or third-person) may refer to:

 A grammatical person, he, she, and they in many languages
 Third-person narrative, a perspective in story-telling
 Third-person view, a point-of-view in film, television, and video games that is shown outside any of the actors or characters.
 Third-person shooter, a genre of 3D computer and video games in which the onscreen character is seen at a distance from a number of different possible angles
 Third Person, a New York City improvising trio

Related pages
 First person (disambiguation)
 Second person (disambiguation)
Sir Frederick William Pottinger, 2nd Baronet (1831–1865), police inspector, was born on April 27, 1831, in India. He became famous for leading the New South Wales police in their hunt for the bushrangers Ben Hall, John Gilbert, Frank Gardiner and John Dunn.

Early life
He was the son of Lieutenant-General Sir Henry Pottinger of the British East India Company, and his wife Susanna Maria, née Cooke, of Dublin. Henry Pottinger was the first Governor of Hong Kong. Frederick went to Eton from 1844 to 1847. Between 1850 and 1854, Pottinger was an officer in the Grenadier Guards in England. He lost a lot of his mother's money gambling on horse races. In 1856 he took the place of his father as second baronet. He soon gambled away all his money. He owed people a lot of money.

Policeman
He went to Australia to search for gold. He joined the New South Wales police force as a mounted trooper. He was an excellent horse rider and spent the next few years working on the gold escort between Gundagai and Goulburn.
People thought Pottinger made a lot of mistakes. He was in trouble for not having enough police guards on the gold escort and letting his prisoners escape. On the night of August 9,  Pottinger and a group of police surrounded the house of Gardiner's mistress, Kate Brown. The bushranger escaped when Pottinger's pistol fired accidentally. They arrested a young boy who they thought was a friend of the bushrangers. When he died from a fever in gaol in March 1863, this was seen to be caused by Pottinger's cruelty. In February 1862 Pottinger was in court at Yass for assault. During a game of billiards he had been called a cheat, a liar and a scoundrel. He hit the person with the billiard cue and smashed his head through a window.  On September 27, 1862, Pottinger had appeared before a Bathurst court on a charge of assault. In February 1863 Pottinger went to Sydney for the trial of the escort robbers. He was pushed in the street by people in the crowd. He also threatened a Member of Parliament, J. J
. J. Harpur, with his whip. He didn't like things that Harpur had said about him.
Sacked
The bushrangers in his area became more active. He captured Patrick Daley. On August 17, 1864, he nearly caught James Alpin McPherson.
In May 1863 the inspector-general told police to think of new ways to catch the bushrangers. Early in January 1865 Pottinger rode a horse in the Wowingragong races. This was against the rules of the police force. He was sacked (lost his job) on February 16, 1865. Pottinger said that he was just trying to get Ben Hall and John Dunn to come out of hiding. His plan worked and the bushrangers went to the races, only Pottinger didn't see them. Protest meetings against his sacking were held on the gold diggings and in the towns. People signed petitions for him to get his job back. He was regarded as a brave and tireless policeman. Pottinger went to Sydney to try and get back his job. On March 5, 1865, at Wascoe's Inn in the Blue Mountains Pottinger accidentally shot himself in the stomach while trying to get onto a moving coach. He was moved to the Victoria Club in Sydney where he died on April 9, 1865. His brother Henry became the 3rd Baronet. He was buried at St Jude's Anglican Church, Randwick.

References
References

Other websites
  Photographs of Sir Frederick Pottinger at the National Library of Australia

1831 births
1865 deaths
Australian police officers
A bushfire is a wildfire that happens mostly in the Australian bush. Bush is the word for scrub, woodland or grassland of Australia and New Zealand. In south east and south west of Australia, bushfires are most common in the summer and early autumn. In the north of Australia, bushfires usually happen during winter which is the dry season. Many Australian plants burn easily. Eucalyptus trees have a high oil content which makes them burn. Over the last 40 years, Australian bushfires have cost around 2.5 billion Australian dollars. In the same period, Australian bushfires caused the death of around 250 people and the destruction of 4,554 homes.But these figures do not include the 173 people who died and the 2000 homes destroyed in the 2009 Victorian bushfires. They can also kill wildlife including rare koalas.

Related pages 
Wildfire
Fire ecology

References

Fire
Ecology
Artificial selection is the intentional breeding of plants or animals. It means the same thing as selective breeding and is an ancient method of genetic engineering. 

Selective breeding is a technique used when breeding domesticated animals, such as dogs, pigeons or cattle. Some of these animals may have traits that a breeder will want to carry over to the next generation. The breeder will therefore select those animals to breed which have that quality, rather than those that do not.

Inbreeding is a particular kind of selective breeding, designed to produce a population which is genetically virtually identical.  Such populations are rarely viable outside the laboratory.  They lose fertility.  To control this, most breeds are outcrossed or backcrossed to wild-type individuals, or at least less inbred stock. The details differ from breed to breed.  The improvement when outcrossing is done is called hybrid vigour.
Charles Darwin used the example of artificial selection to introduce his idea of natural selection. Artificial selection may be contrasted with the process of natural selection. In natural selection, the differential reproduction of organisms with certain traits happens. This is because some variations help the organism to have better survival and reproduction. The process maintains or enhances the fitness of a population in its natural habitat. 

Artificial selection may sometimes be unintentional; it is thought that domestication of crops by early humans was largely unintentional.

References 

Classical genetics
Domesticated animals
Agriculture
A selectable marker is a reporter gene introduced into a cell along with a gene insert. What this means is, the experimenter can tell the right gene is in the cell because the marker can be seen or detected.

Most often, this is used for  bacteria or for cells in culture. Selectable markers show the success of a transfection or other procedure meant to introduce foreign DNA into a cell. It is a technique in gene targeting and gene knockout.

Selectable markers are often antibiotic resistance genes; bacteria that have been subjected to a procedure to introduce foreign DNA are grown on a medium containing an antibiotic.  The antibiotic knocks out cells which do not have the resistant marker. Those bacterial colonies that can grow have successfully taken up and expressed the introduced genetic material.

An alternative to a selectable marker is a screenable marker, which allows the researcher to distinguish between wanted and unwanted cells.
Examples of selectable markers include:
Beta-lactamase which confers ampicillin resistance to bacterial hosts.
Neo gene from Tn5, which confers antibiotic resistance to kanamycin in bacteria and to geneticin in eukaryote cells.
Mutant FabI gene (mFabI) from E. coli genome, which confers triclosan resistance to the host.

References 

Genetic engineering
In Computer Science, especially in artificial intelligence, evolutionary algorithms are a kind of algorithm that simulate evolution to optimise something. Each generation of solution is subjected to some kind of fitness function; those that survive are then recombined in some way to make the next generation of solution. This is done until a certain level of fitness is reached, or a determined number of generations have been used.

Artificial intelligence
Artificial life (commonly Alife or alife) is a field of study that looks at systems related to life, its processes, and its evolution. This is done with simulations using computer models, robotics, and biochemistry. There are three main kinds of alife, named for their approaches: soft, (from software); hard, (from hardware); and wet (from biochemistry). Artificial life simulates traditional biology by trying to recreate biological phenomena. It is common for "artificial life" to refer to soft alife in specific.

References

Scientific modeling
Nils Aall Barricelli (24 January 1912 – 27 January 1993) was a Norwegian-Italian mathematician.

Barricelli's early computer-assisted experiments in symbiogenesis and evolution are considered pioneering in artificial life research. Barricelli, who was independently rich, held an unpaid residency at the Institute for Advanced Study in Princeton, New Jersey in 1953, 1954. and 1956. He later worked at the University of California, Los Angeles, at Vanderbilt University (until 1964), in the Department of Genetics of the University of Washington, Seattle (until 1968) and then at the Mathematics Institute of the University of Oslo. Barricelli published in a variety of fields including virus genetics, DNA, theoretical biology, space flight, theoretical physics and mathematical language.

References

Other websites
 Barricelli's Universe (make-digital.com) 
 George Dyson TED talk discusses Barricelli

1912 births
1993 deaths
Norwegian mathematicians
Italian mathematicians
A genetic algorithm is an algorithm that imitates the process of natural selection. They help solve optimization and search problems. Genetic algorithms are part of the bigger class of evolutionary algorithms. Genetic algorithms imitate natural biological processes, such as inheritance, mutation, selection and crossover.

The concept of genetic algorithms is a search technique often used in computer science to find complex, non-obvious solutions to algorithmic optimisation and search problems. Genetic algorithms are global search heuristics.
What is it 
Natural evolution can be modeled as a game, in which the rewards for an organism that plays a good game of life are the passing on of its genetic material to its successors and its continued survival. In natural evolution, how well an individual performs depends on its who it works with and who it competes with, as well as the environment. Genetic algorithms are a simulation of natural selection on a population of candidate solutions to an optimization problem (chromosomes, individuals, creatures, or phenotypes).
Candidates are evaluated and crossbred in an attempt to make good solutions. Such solutions may take a lot of time and are not obvious to a human. An evolutionary phase is started with a population of randomly generated beings. In each generation, the fitness of every individual in the population is evaluated. Some are randomly selected from the current population (based on their fitness) and modified (recombined and possibly randomly mutated) to form a new population. The cycle repeats with this new population. The algorithm ends either after a set number of generations have passed, or a good fitness level has been reached for the population. If the algorithm has ended due to reaching a maximum number of generations, it does not necessarily mean a good fitness level has been obtained.

Programming this on a computer 
The pseudocode is:
Initialization: Some possible solutions are created; very often these will have random values
Evaluation: A fitness function scores each solution; the score will be a number that tells how well this solution solves the problem.
The following steps are run until the requirement to stop is met:
Selection: Pick the solutions/individuals for the next iteration
Recombination: Combine the solutions picked
Mutation: Randomly change the newly created solutions
Evaluation: Apply the fitness function, see step 2.
If the requirement to stop is not met, re-start with the selection step.

Example 
The above description is abstract. A concrete example helps.

Applications

In general 
Genetic algorithms are good at solving problems that include timetabling and scheduling. They have also been applied to engineering. They are often used to solve global optimization problems.
As a general rule of thumb, genetic algorithms might be useful in problem domains that have a complex fitness landscape as mixing is designed to move the population away from local optima that a traditional hill climbing algorithm might get stuck in. Commonly used crossover operators cannot change any uniform population. Mutation alone can provide ergodicity of the overall genetic algorithm process (seen as a Markov chain).

Examples of problems solved by genetic algorithms include: mirrors designed to funnel sunlight to a solar collector, antennae designed to pick up radio signals in space, walking methods for computer figures, optimal design of aerodynamic bodies in complex flowfields
In his Algorithm Design Manual, Skiena advises against genetic algorithms for any task: "It is quite unnatural to model applications in terms of genetic operators like mutation and crossover on bit strings. The pseudobiology adds another level of complexity between you and your problem. Second, genetic algorithms take a very long time on nontrivial problems. [...] The analogy with evolution—where significant progress require [sic] millions of years—can be quite appropriate.
[...] I have never encountered any problem where genetic algorithms seemed to me the right way to attack it. Further, I have never seen any computational results reported using genetic algorithms that have favorably impressed me. Stick to simulated annealing for your heuristic search voodoo needs."
Board games 
Board games are a very relevant part of the area of genetic algorithms as applied to game theory problems. Much of the early work on computational intelligence and games was directed toward classic board games, such as tic-tac-toe, chess, and checkers. Board games can now, in most cases, be played by a computer at a higher level than the best humans, even with blind exhaustive search techniques. Go is a noted exception to this tendency, and has so far resisted machine attack. The best Go computer players now play at the level of a good novice. Go strategy is said to rely heavily on pattern recognition, and not just logical analysis as with chess and other more piece-independent games. The huge effective branching factor required for finding high quality solutions heavily restricts the look-ahead that can be used within a move sequence search.
Computer games 
The genetic algorithm can be used in computer games to create artificial intelligence (the computer plays against you). This allows for a more realistic game experience; if a human player can find a sequence of steps which always lead to success even when repeated in different games, there can be no challenge left. Conversely if a learning technique such as a genetic algorithm for a strategist can avoid repeating past mistakes, the game will have more playability.

Genetic algorithms require the following parts:

 A method for representing the challenge in terms of the solution (e.g. routing soldiers in an attack in a strategy game)
 A fitness or evaluation function in order to determine the quality of an instance (e.g. a measurement of damage done to an opponent in such an attack).
The fitness function accepts a mutated instantiation of an entity and measures its quality. This function is customized to the problem domain. In many cases, particularly those involving code optimization, the fitness function may simply be a system timing function. Once a genetic representation and fitness function are defined, a genetic algorithm will instantiate initial candidates as described before, and then improve through repetitive application of mutation, crossover, inversion and selection operators (as defined according to the problem domain).

Limitations 
There are limitations of the use of a genetic algorithm compared to alternative optimization algorithms:
Repeated fitness function evaluation for complex problems is often the most limiting segment of artificial evolutionary algorithms. Finding the optimal solution to complex problems often requires very expensive fitness function evaluations. In real world problems such as structural optimization problems, a single function evaluation may require several hours to several days of complete simulation. Typical optimization methods cannot deal with such types of problem. It is often necessary to use approximation, as calculating the exact solution takes too long. Genetic algorithms sometimes combine different approximation models to solve complex real life problems.
Genetic algorithms do not scale well. That is, where the number of elements which are exposed to mutation is large there is often an exponential increase in search space size. This makes it extremely difficult to use the technique on problems such as designing an engine, a house or a plane. To use genetic algorithms with such problems,  they must be broken down into the simplest representation possible. For this reason, we see evolutionary algorithms encoding designs for fan blades instead of engines, building shapes instead of detailed construction plans, and airfoils instead of whole aircraft designs. The second problem of complexity is the issue of how to protect parts that have evolved to represent good solutions from further destructive mutation, particularly when their fitness assessment requires them to combine well with other parts.
 The "better" solution is only in comparison to other solutions. As a result, the stop criterion is not clear in every problem.
In many problems, genetic algorithms have a tendency to converge towards local optima or even arbitrary points rather than the global optimum of the problem. This means that the algorithm does not "know how" to sacrifice short-term fitness to gain longer-term fitness. The likelihood of this occurring depends on the shape of the fitness function: certain problems make it easy to find the global optimum, others may make it easier for the function to find the local optima. This problem may be lessened by using a different fitness function, increasing the rate of mutation, or by using selection techniques that maintain a diverse population of solutions. A common technique to maintain diversity is to use a "niche penalty": any group of individuals of sufficient similarity (niche radius) have a penalty added, which will reduce the representation of that group in the following generations, permitting other (less similar) individuals to be kept in the population
. This trick, however, may not be effective, depending on the landscape of the problem. Another possible technique would be to simply replace part of the population with randomly generated individuals, when most of the population is too similar to each other. Diversity is important in genetic algorithms (and genetic programming) because crossing over a homogeneous population does not yield new solutions. In evolution strategies and evolutionary programming, diversity is not essential because of a greater reliance on mutation.
Operating on dynamic data sets is difficult, as genomes begin to converge early on towards solutions which may no longer be valid for later data. Several methods have been proposed to fix this by increasing genetic diversity somehow and preventing early convergence, either by increasing the probability of mutation when the solution quality drops (called triggered hypermutation), or by occasionally introducing entirely new, randomly generated elements into the gene pool (called random immigrants). Again, evolution strategies and evolutionary programming can be implemented with a so-called "comma strategy" in which parents are not maintained and new parents are selected only from offspring. This can be more effective on dynamic problems.
Genetic algorithms cannot effectively solve problems in which the only fitness measure is a single right/wrong measure (like decision problems), as there is no way to converge on the solution (no hill to climb). In these cases, a random search may find a solution as quickly as a GA. However, if the situation allows the success/failure trial to be repeated giving (possibly) different results, then the ratio of successes to failures provides a suitable fitness measure.
For specific optimization problems and problem instances, other optimization algorithms may be more efficient than genetic algorithms in terms of speed of convergence. Alternative and complementary algorithms include evolution strategies, evolutionary programming, simulated annealing, Gaussian adaptation, hill climbing, and swarm intelligence (e.g.: ant colony optimization, particle swarm optimization) and methods based on integer linear programming. The suitability of genetic algorithms is dependent on the amount of knowledge of the problem; well known problems often have better, more specialized approaches.
In 1950, Alan Turing proposed a "learning machine" which would parallel the principles of evolution. Computer simulation of evolution started as early as in 1954 with the work of Nils Aall Barricelli, who was using the computer at the Institute for Advanced Study in Princeton, New Jersey.  His 1954 publication was not widely noticed. Starting in 1957,  the Australian quantitative geneticist Alex Fraser published a series of papers on simulation of artificial selection of organisms with multiple loci controlling a measurable trait. From these beginnings, computer simulation of evolution by biologists became more common in the early 1960s, and the methods were described in books by Fraser and Burnell (1970) and Crosby (1973). Fraser's simulations included all of the essential elements of modern genetic algorithms. In addition, Hans-Joachim Bremermann published a series of papers in the 1960s that also adopted a population of solution to optimization problems, undergoing recombination, mutation, and selection
. Bremermann's research also included the elements of modern genetic algorithms. Other noteworthy early pioneers include Richard Friedberg, George Friedman, and Michael Conrad. Many early papers are reprinted by Fogel (1998).
Although Barricelli, in work he reported in 1963, had simulated the evolution of ability to play a simple game, artificial evolution became a widely recognized optimization method as a result of the work of Ingo Rechenberg and Hans-Paul Schwefel in the 1960s and early 1970s – Rechenberg's group was able to solve complex engineering problems through evolution strategies.  Another approach was the evolutionary programming technique of Lawrence J. Fogel, which was proposed for generating artificial intelligence. Evolutionary programming originally used finite state machines for predicting environments, and used variation and selection to optimize the predictive logics. Genetic algorithms in particular became popular through the work of John Holland in the early 1970s, and particularly his book Adaptation in Natural and Artificial Systems (1975). His work originated with studies of cellular automata, conducted by Holland and his students at the University of Michigan
. Holland introduced a formalized framework for predicting the quality of the next generation, known as Holland's schema theorem. Research in GAs remained largely theoretical until the mid-1980s, when The First International Conference on Genetic Algorithms was held in Pittsburgh, Pennsylvania.
References 
  Herrera, F.; Lozano, M.; and Verdegay, J. L. 1998. Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artif. Intell. Rev. 12(4):265–319.
  Lucas, S., and Kendell, G. 2006. Evolutionary computation and games. In IEEE Comput Intell Mag. February, 10–18. IEEE.
  Yao, X. Recent new development in evolutionary programming.
  Samuel, A. L. 1995. Some studies in machine learning using the game of checkers. 71–105.
  Muller, M. 2002. Computer go. Artif. Intell. 134(1-2):145–179.
  Bouzy, B., and Cazenave, T. 2001. Computer go: an AI oriented survey. Artificial Intelligence 132:39–103.

Sources

Artificial intelligence
Ingo Rechenberg''' (born January 20 1934 in Berlin) is a German computer scientist and professor. Rechenberg is 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 educated at the Technical University of Berlin and at the University of Cambridge. Since 1972 he has been a full professor at the  Technical University of Berlin, where he is heading the Department of Bionics and Evolution Techniques.
His awards include the Lifetime Achievement Award of the Evolutionary Programming Society (US, 1995) and the Evolutionary Computation Pioneer Award of the IEEE Neural Networks Society (US, 2002). In 1954, Rechenberg also became world champion in the field of model airplanes.

Selected bibliography 

 Ingo Rechenberg (1971): Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis). Reprinted by Fromman-Holzboog (1973).
 Ingo Rechenberg: Evolutionsstrategie '94. Stuttgart: Frommann-Holzboog 1994.

References

Other websites 

Rechenberg's Bionic's Lab in Berlin  (in German) and in English 

1934 births
Living people
Evolution strategies are ways to solve a problem using evolutionary algorithms. The term was coined by Ingo Rechenberg.

Artificial intelligence
John Henry Holland (February 2, 1929 – August 9, 2015) was an American scientist. He was Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He was a pioneer in complex system and nonlinear science. He was known as the father of genetic algorithms.

Holland died on August 9, 2015 in Ann Arbor, Michigan, aged 86.

References

1929 births
2015 deaths
American psychologists
Scientists from Indiana
Ontario Lacus is a lake filled with methane and ethane near the south pole of Saturn's moon Titan.  It was identified as a hydrocarbon lake and confirmed by data from the Cassini spacecraft. This discovery was announced in the 31 July 2008 edition of Nature. Ontario Lacus is about 20,000 km2 in area, and is named after Lake Ontario in North America, which is a similar size and shape.

References

Other websites

Saturn's moons
Lakes
A penetrometer is a device to test the strength of a material.

It is often used on soil. The penetrometer is dropped on soil or it is pressed against soil to measure how deep a hole it makes. This is a way to decide if soil is strong enough to build a road on. Scientists can use a penetrometer to measure how much moisture is in soil. Penetrometers are used on space probes such as the Cassini-Huygens probe, to measure the amount of moisture in soil on other planets.

Sources 
University of Arizona document, "An impact penetrometer for a landing spacecraft". 
A picture of a penetrometer from the European Space Agency (ESA) 

Geology
Measuring tools
Crème brûlée (or Crème brulée) is a dessert. It is made of custard. It is topped with a thin, brittle crust of caramelized sugar. It may be served cold, warm, or at room temperature. The custard is baked, and then sprinkled with sugar Casa  that is caramelized under a broiler or with a kitchen blowtorch. The custard is flavored with vanilla. Other flavorings include chocolate, orange liqueur, fruit, or other. The caramelized sugar crust can be covered with a liqueur and set on fire.

The exact origin of crème brûlée is uncertain. France, England, and Spain all claim to be the country where crème brûlée had its origin. The first printed recipe for a dessert called crème brûlée is from the 1691 edition of the French cookbook Le Cuisinier Royal et Bourgeois by Francois Massialot, a cook at the Palace of Versailles. That version was a sweet custard of egg yolks and milk with a burnt sugar crust. It is similar to the modern versions. In the second edition of the book, the dessert is called crème anglaise.
At Trinity College, Cambridge the dessert was known as 'burnt cream'. The college crest was burnt into sugar on top of a custard using a hot iron. It may predate Massialot's cookbook with one authority claiming it was first served at Trinity in 1630.

Catalans claim their dessert known as crema catalana is the original. It is a rich custard often flavoured with cinnamon and lemon or orange zest topped with carmelized sugar. The Catalan version was first recorded in the 18th century. It is also called Crema de Sant Josep, after Saint Joseph, the husband of the Virgin Mary. In Spain and in many other parts of Europe, Saint Joseph’s Day is celebrated on 19 March. Crema Catalana is a traditional dessert served on this day.

References

Other websites 

A guide to crème brûlée history and how to prepare a recipe 

Desserts