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Other problem-solving techniques Concisely stated, a genetic algorithm or GA for short is a programming technique that mimics biological evolution as a problem-solving strategy. Given a specific problem to solve, the input to the GA is a set of potential solutions to that problem, encoded in some fashion, and a metric called a fitness function that allows each candidate to be quantitatively evaluated.
These candidates may be solutions already known to work, with the aim of the GA being to improve them, but more often they are generated at random. The GA then evaluates each candidate according to the fitness function. In a pool of randomly generated candidates, of course, most will not work at all, and these will be deleted.
However, purely by chance, a few may hold promise - they may show activity, even if only weak and imperfect activity, toward solving the problem.
These promising candidates are kept and allowed to reproduce. Multiple copies are made of them, but the copies are not perfect; random changes are introduced during the copying process. These digital offspring then go on to the next generation, forming a new pool of candidate solutions, and are subjected to a second round of fitness evaluation.
Those candidate solutions which were worsened, or made no better, by the changes to their code are again deleted; but again, purely by chance, the random variations introduced into the population may have improved some individuals, making them into better, more complete or more efficient solutions to the problem at hand.
Again these winning individuals are selected and copied over into the next generation with random changes, and the process repeats. The expectation is that the average fitness of the population will increase each round, and so by repeating this process for hundreds or thousands of rounds, very good solutions to the problem can be discovered.
As astonishing and counterintuitive as it may seem to some, genetic algorithms have proven to be an enormously powerful and successful problem-solving strategy, dramatically demonstrating the power of evolutionary principles.
Genetic algorithms have been used in a wide variety of fields to evolve solutions to problems as difficult as or more difficult than those faced by human designers.
Moreover, the solutions they come up with are often more efficient, more elegant, or more complex than anything comparable a human engineer would produce. In some cases, genetic algorithms have come up with solutions that baffle the programmers who wrote the algorithms in the first place!
Methods of representation Before a genetic algorithm can be put to work on any problem, a method is needed to encode potential solutions to that problem in a form that a computer can process.
One common approach is to encode solutions as binary strings: Another, similar approach is to encode solutions as arrays of integers or decimal numbers, with each position again representing some particular aspect of the solution.
This approach allows for greater precision and complexity than the comparatively restricted method of using binary numbers only and often "is intuitively closer to the problem space" Fleming and Purshousep. This technique was used, for example, in the work of Steffen Schulze-Kremer, who wrote a genetic algorithm to predict the three-dimensional structure of a protein based on the sequence of amino acids that go into it Mitchellp.
Schulze-Kremer's GA used real-valued numbers to represent the so-called "torsion angles" between the peptide bonds that connect amino acids. A protein is made up of a sequence of basic building blocks called amino acids, which are joined together like the links in a chain.
Once all the amino acids are linked, the protein folds up into a complex three-dimensional shape based on which amino acids attract each other and which ones repel each other. The shape of a protein determines its function.
Genetic algorithms for training neural networks often use this method of encoding also. A third approach is to represent individuals in a GA as strings of letters, where each letter again stands for a specific aspect of the solution. One example of this technique is Hiroaki Kitano's "grammatical encoding" approach, where a GA was put to the task of evolving a simple set of rules called a context-free grammar that was in turn used to generate neural networks for a variety of problems Mitchellp.
The virtue of all three of these methods is that they make it easy to define operators that cause the random changes in the selected candidates: See the section on Methods of change for more detail about the genetic operators.
Another strategy, developed principally by John Koza of Stanford University and called genetic programming, represents programs as branching data structures called trees Koza et al.
In this approach, random changes can be brought about by changing the operator or altering the value at a given node in the tree, or replacing one subtree with another. Three simple program trees of the kind normally used in genetic programming. The mathematical expression that each one represents is given underneath.
It is important to note that evolutionary algorithms do not need to represent candidate solutions as data strings of fixed length. Some do represent them in this way, but others do not; for example, Kitano's grammatical encoding discussed above can be efficiently scaled to create large and complex neural networks, and Koza's genetic programming trees can grow arbitrarily large as necessary to solve whatever problem they are applied to.
Methods of selection There are many different techniques which a genetic algorithm can use to select the individuals to be copied over into the next generation, but listed below are some of the most common methods.
Some of these methods are mutually exclusive, but others can be and often are used in combination. The most fit members of each generation are guaranteed to be selected. Most GAs do not use pure elitism, but instead use a modified form where the single best, or a few of the best, individuals from each generation are copied into the next generation just in case nothing better turns up.Koobface is a network worm that attacks Microsoft Windows, Mac OS X, and Linux platforms.
This worm originally targeted users of networking websites like Facebook, Skype, Yahoo Messenger, and email websites such as GMail, Yahoo Mail, and AOL ashio-midori.com also targets other networking websites, such as MySpace, Twitter, and it can infect other devices on the same local network.
(ed note: this is a commentary about the computer game Children of a Dead Earth).
I see a lot of misconceptions about space in general, and space warfare in . Computer virus analysis Computer Viruses. Fig 1. The infected hosts of the SQL Slammer virus after 30 minutes. The size of the circles denotes the relative number of the infected hosts.
(source: CAIDA). Computer viruses (hereafter called viruses) are programs that have some bad effects on a computer. Originally viruses were spread slowly. The human brain is the central organ of the human nervous system, and with the spinal cord makes up the central nervous ashio-midori.com brain consists of the cerebrum, the brainstem and the ashio-midori.com controls most of the activities of the body, processing, integrating, and coordinating the information it receives from the sense organs, and making decisions as to the instructions sent to the.
List of common misconceptions.
Jump to navigation Jump to search This is a list of common Computers running macOS or Linux are not immune to malware such as trojan horses or computer viruses. While Microsoft Windows has a much larger number of viruses developed for it. Analysis of Web Application Worms and Viruses Billy Hoffman ([email protected] •People tend to have a lot of misconceptions about web application security –SSL Overview of Web Application Worms and Viruses.
Web Worms and Web Viruses.