Inheritance (genetic algorithm) - Inheritance (genetic algorithm) In genetic algorithms, inheritance is the ability of modelled objects to mate, mutate and propagate their problem solving genes to the next generation, in order to produce an evolved solution to a particular problem..
Genetic algorithm - Genetic algorithm A genetic algorithm (GA) is an algorithm used to find approximate solutions to difficult-to-solve problems, inspired by and named after biological processes of inheritance, mutation, natural selection, and the genetic crossover that occurs when parents mate to produce offspring. Genetic algorithms are a particular class of evolutionary algorithms. Genetic algorithms are typically implemented as a computer simulation in which a population of abstract representations of candidate solutions to an optimization problem are stochastically selected, recombined, mutated, and then either eliminated or retained, based on their relative fitnesses. John Holland was the pioneering founder of much of today's work in genetic algorithms, which has moved on from a purely theoretical subject (though based on computer modelling), to provide methods which can be used to solve.
Emergent algorithm - Emergent algorithm An emergent algorithm is an algorithm that has the following characteristics: it achieves predictable global effects; it does not require global visibility; it does not assume any kind of centralized control; it is self-stabilizing. See also evolutionary computation genetic algorithm heuristic.
Evolutionary algorithm - Evolutionary algorithm An evolutionary algorithm (also EA, Evolutionary Computation, Artificial Evolution) is an algorithm using evolutionary techniques inspired by mechanisms from biological evolution such as natural selection, mutation and recombination to find an optimal configuration for a specific system within specific constraints. Evolutionary algorithms include: genetic programming and genetic algorithms which use the gene transmission and mutation mechanism as an optimization technique evolutionary programming, which allows one to parameterize computer programs to find optimal solutions according to a goal function. Most of these techniques are similar in spirit, but differ largely in the details of their implementation and the nature of the particular problem domains they have been applied to. Evolutionary algorithms are often used to design engineering systems in the place of manual design where the.
Algorithm - Algorithm Broadly-defined, an algorithm is an interpretable, finite set of instructions for dealing with contingencies and accomplishing some task which can be anything that has a recognizable end-state, end-point, or result for all inputs. (contrast with heuristic). Algorithms often have steps that repeat (iterate) or require decisions (logic and comparison) until the task is completed. In formal mathematical terms, an algorithm is considered to be any sequence of operations which can be performed by a Turing-complete system. Different algorithms may complete the same task with a different set of instructions in more or less time, space, or effort than others. A cooking recipe is an example of an algorithm. Given two different recipes for making potato salad, one may have "peel the potato" before "boil the.
Ant colony algorithm - Ant colony algorithm The ant colony algorithm is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphss. They are inspired by the behavior of ants in finding paths from the colony to food. Overview In the real world, ants lay down pheromone trails as they search for food. Each ant wanders randomly, but is more likely to travel a path that has pheromone on it. Thus, when one ant finds a good (short) path from the colony to a food source, other ants are more likely to follow that path; positive feedback eventually leaves all the ants following a single path. The idea of the ant colony algorithm is to mimic this behavior with "simulated ants" walking around the graph.
Inheritance (computer science) - bubble (circle). The classes [Socrates] and [Man] are placed in rectangles. Some computer scientists, such as the principal designer of CLU, Barbara Liskov, urge that the use of inheritance be restricted to those designs which truly reflect the problem being solved, and that re-use and subtype polymorphism are not actually the strong points of a design resting solely on inheritance. The most widely encountered practical application of inheritance is in word processors, where people often don't realize except intuitively that the components of the document are inheriting layout and style properties from their parent elements, not even when they are using style sheets for formatting. A similar impression can be seen with drawing programs. Usage in different fields Inheritance (object-oriented programming) -- in computer programming it is part of the extremely.
Ga - ISO country code gallium (Ga), symbol for the chemical element genetic algorithm Georgia, abbreviation for the U.S. state (Ga.) or state code (GA).
GSA - GSA may stand for: Gale-Shapley Algorithm Game Spy Arcade (game utility) GameSpy Arcade (gaming service) Gaming Standards Association Gamma Sigma Alpha Garden Seed Association Garden State Academy (New Jersey) Gay-Straight Alliance Network General sales agent General Services Administration (US, gsa.gov) General somatic afferent (nerve) Genetic sexual attraction Genetics Society of America Genetics Society of Australia Geological Society of America Geological Society of Australia (gsa.org.au) Georgia Securities Association Georgia Shrimp Association Georgia Society of Anesthesiologists Georgia Sociological Association Georgia Strait Alliance German Studies Association Gerontological Society of America Girl Scouts of America Girls’ Schools Association (UK) Glasgow School of Art (gsa.ac.uk) Global Sports Alliance (Japan, gsa.or.jp) Goethe- und Schiller-Archiv Good Shepherd Academy Google Search Appliance Graduate student association Greater Shadow Amuli (Asheron's Call) Grover Search Algorithm Guam Society of America.
Fibonacci number - a shift of indices. In words: you start with two ones, and then produce the next Fibonacci number by adding the two previous Fibonacci numbers. The first Fibonacci numbers are 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657... Table of contents showTocToggle("show","hide") 1 Origins 2 Explicit formula 3 Computing Fibonacci numbers 4 Applications 5 Generalizations 6 Algorithm 7 Identities Origins This sequence was first described by Leonardo of Pisa, who was known as Fibonacci (ca. 1200), to describe the growth of a rabbit population. The numbers describe the number of pairs in a (somewhat idealized) rabbit population after n months if it is assumed that the first month there is just one newly born pair,.
Fitness landscape - remains, unless a rare mutation opens a path to a new, higher fitness peak. Note, however, that at high mutation rates this picture is somewhat simplistic. A population may not be able to climb a very sharp peak if the mutation rate is too high, or it may drift away from a peak it had already found. The process of drifting away from a peak is often referred to as Muller's ratchet. Fitness landscapes in evolutionary optimization Apart from the field of evolutionary biology, the concept of a fitness landscape has also gained importance in evolutionary optimization methods such as genetic algorithms or evolutionary strategies. In evolutionary optimization, one tries to solve real-world problems (e.g., engineering or logistics problems) by imitating the dynamics of biological evolution. For example, a delivery truck.
Fitness proportionate selection - also known as roulette wheel selection, is a genetic operator used in genetic algorithms for selecting potentially useful solutions for recombination. In fitness proportionate selection, as in all selection methods, possible solutions or chromosomes are assigned a fitness by the fitness function. In fitness proportionate selection, this fitness level is used to associate a probability of selection with each individual chromosome. While candidate solutions with a higher fitness will be less likely to be eliminated, there is still a chance that they may be. Contrast this with a less sophisticated selection algorithm, such as truncation selection, which will eliminate a fixed percentage of the weakest candidates. With fitness proportionate selection there is a chance some weaker solutions may survive the selection process; this is an advantage, as though a solution may.
Fitness function - fitness function quantifies the optimality of a solution (that is, a chromosome) in a genetic algorithm. An ideal fitness function correlates closely with the algorithm's goal and yet may be computed quickly. A fitness function is sometimes referred to as an objective function in the context of genetic algorithms..
FreeCell - the foundations of the different color are greater than the card face value minus 2, and the value of the other foundation of the same color is greater than the card face value minus 3. Culture FreeCell has spanned a great deal of culture around it. The most important information about it can be found in the Freecell FAQ which is maintained by Michael Keller. The Microsoft version could deal 32,000 numbered deals, and so most efforts were concentrated on analyzing their behavior. The Internet FreeCell Project by Dave Ring, which was finished in October 1995, tried to analyze which of the Microsoft deals were solvable. Ring assigned 100 consecutive games chunks across volunteering human solvers and collected the games that they reported to be unsolvable, and assigned them to other.
Evolutionary art - to create an artwork which continually changes according to an evolutionary algorithm. In common with natural selection and animal husbandry, the members of a population undergoing artificial evolution modify their form or behaviour over many reproductive generations in response to a selective regime. In Interactive Evolution the selective regime may be applied by the viewer explicitly by selecting individuals which are aesthtically pleasing, as in Richard Dawkins' Biomorphs program. Alternatively a selection pressure can be generated implicitly, for example according to the length of time a viewer spends near a piece of evolving art. Equally, evolution may be employed as a mechanism for generating a dynamic world of adaptive individuals, in which the selection pressure is imposed by the program, and the viewer plays no role in selection, as in the.
2001 in science - moon Io. Biology The publicly funded Human Genome Project, led by Francis Collins and the privately funded Celera effort, led by Craig Venter simultaneously publish their decoding of the human genome (in Nature and Science, respectively). Craig Venter and Mark Adams complete the genetic map of the laboratory mouse. Fossil remains of the whale Rodhocetus Balochistanensis found in Balochistan Province, Pakistan by Philip Gingerich. Computer science In quantum computing, the first working 7-qubit NMR computer is demonstrated at IBM's Almaden Research Center, demonstrating Shor's algorithm Medicine July 2 - World's first self-contained artificial heart implanted in Robert Tools. Awards Nobel Prizes Physics - Eric A Cornell, Wolfgang Ketterle, Carl E Wieman Chemistry - William S. Knowles, Ryoji Noyori, K. Barry Sharpless Medicine - Leland H. Hartwell, R. Timothy Hunt, Paul M..
Automatic label placement - or even removing the label. Then, it must select a set of placements that results in the least overlap, and has other desirable properties. For every but the most trivial setups, the problem is NP-Hard. The simplest greedy algorithm places consecutive labels on the map in positions that result in minimal extra overlap of labels. Its results are not satisfactory even for very simple problems, but it is extremely fast. Slightly more complex algorithms rely on local optimization to reach a local optimum of a placement evaluation function - in each iteration placement of a single label is moved to another position, and if it improves the result, the move is preserved. It performs reasonably well for maps that are not too densely labelled. Slightly more complex variations try moving 2.
Case-based reasoning - the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue -- an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his newfound procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands. At first glance, CBR may seem similar to the rule-induction algorithms of machine learning.2 Like a rule-induction algorithm, CBR starts with a set of.
Computer virus - written viruses employ different kinds of obfuscation. Some old viruses (especially in MS-DOS) alters the information attached to the files it infect, last updated and the filesize. Antivirus software that just searched through recently edited files or files that has changed in size will not notice the virus presence in this case. Note that changing the information on the size of the file is not the same thing as actually changing the size of the file under MS-DOS. This approach does not fool current antivirus software. Another hiding technique, and at the same time an easy way to spread for old viruses, was to infect the hard disk drive instead of the files saved on it. At bootstrap the computer runs the code located in the boot sector, which is replaced.
Traveling salesman problem - "good" solutions with "high" probability, have been devised. An approximative solution for 15,112 cities in Germany was found in 2001 by Princeton University scholars. Algorithms The traditional lines of attack of the NP-hard problems are the following: Devising algorithms for finding exact solutions (they will work reasonably fact only for relatively small problem sizes) Devising "suboptimal" or heuristic algorithms, i.e., algorithms that deliver seemingly or provably good solutions, but which could not proved to be optimal. Finding special cases for the problem ("subproblems") for which either exact or better heuristics are posible. Exact algorithms Various branch-and-bound algorithms, which can be used to process TSPs containing 40-60 cities. Progressive improvement algorithms which use techniques reminiscent of linear programming works well up to 120-200 cities. Heuristics The nearest neighbour algorithm, which is normally.
