This method can be helpful in making the best selection later on when all individuals have relatively high fitness and only small differences in fitness distinguish one from another. Tournament selection : Subgroups of individuals are chosen from the larger population, and members of each subgroup compete against each other. Only one individual from each subgroup is chosen to reproduce. Rank selection : Each individual in the population is assigned a numerical rank based on fitness, and selection is based on this ranking rather than absolute differences in fitness. The advantage of this method is that it can prevent very fit individuals from gaining dominance early at the expense of less fit ones, which would reduce the population's genetic diversity and might hinder attempts to find an acceptable solution. Generational selection : The offspring of the individuals selected from each generation become the entire next generation. No individuals are retained between generations. Steady-state selection : The offspring of the individuals selected from each generation go back into the pre-existing gene pool, replacing some of the less fit members of the previous generation.
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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 hand 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 lost arbitrarily large as necessary to solve whatever problem they. 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. Elitist selection : 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.) Fitness-proportionate selection : More fit. Roulette-wheel selection : A form of fitness-proportionate selection in which the chance of an individual's being selected is proportional to the amount by which its fitness is greater or less than its competitors' fitness. (Conceptually, this can be represented as a game of roulette - each individual gets a slice of the wheel, but more fit ones get larger slices than less fit ones. The wheel is then spun, and whichever individual "owns" the section on which it lands each time is chosen.) Scaling selection : As the average fitness of the population increases, the strength of the selective pressure also increases and the fitness function becomes more discriminating.
(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 reviews set of rules called a context-free grammar that was in turn used to generate neural networks for a variety of problems (. 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: flip a 0 to a 1 or vice versa, add or subtract from the value of a number. (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. 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. Figure 1: Three simple program trees of the kind normally used in genetic programming.
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: sequences of 1's and 0's, where the digit at each position represents the value of some aspect of the solution. 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 diary only and often "is intuitively closer to the problem space" ( Fleming and Purshouse 2002,. 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 ( Mitchell 1996,. Schulze-kremer's ga used real-valued numbers to represent the so-called "torsion angles" between the peptide bonds that connect amino acids.
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. 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.
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no amount of time or genetic change can benefits transform one "kind" into another. However, exactly how the creationists determine what a "kind" is, or what mechanism prevents living things from evolving beyond its boundaries, is invariably never explained. But in the last few decades, the continuing advance of modern technology has brought about something new. Evolution is now producing practical benefits in a very different field, and this time, the creationists cannot claim that their explanation fits the facts just as well. This field is computer science, and the benefits come from a programming strategy called genetic algorithms. This essay will explain what genetic algorithms are and will show how they are relevant to the evolution/creationism debate.
What is a genetic algorithm? Top 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.
Thus we are faced with a mighty challenge. Fortunately we have a history of meeting great challenges using imagination and our irrepressible capacity to adapt. To ensure that we journey in the right direction, we must allow our knowledge, experience and institutions to catch up with the overwhelming progress of science and technology, and learn how to become both good neighbors for each other and good guests of the natural. Just as we are moved by water, we must move quickly in order to save. Introductory article written for civilization, the magazine of the us library of Congress, October-november 2000, by guest Editor mikhail Gorbachev. Introduction, top reationists occasionally charge that evolution is useless as a scientific theory because it produces no practical benefits and has no relevance to daily life.
However, the evidence of biology alone shows that this claim is untrue. There are numerous natural phenomena for which evolution gives us a sound theoretical underpinning. To name just one, the observed development of resistance - to insecticides in crop pests, to antibiotics in bacteria, to chemotherapy in cancer cells, and to anti-retroviral drugs in viruses such as hiv - is a straightforward consequence of the laws of mutation and selection. The evolutionary postulate of common descent has aided the development of new medical drugs and techniques by giving researchers a good idea of which organisms they should experiment on to obtain results that are most likely to be relevant to humans. Finally, the principle of selective breeding has been used to great effect by humans to create customized organisms unlike anything found in nature for their own benefit. The canonical example, of course, is the many varieties of domesticated dogs (breeds as diverse as bulldogs, chihuahuas and dachshunds have been produced from wolves in only a few thousand years but less well-known examples include cultivated maize (very different from its wild relatives, none. Critics might charge that creationists can explain these things without recourse to evolution. For example, creationists often explain the development of resistance to antibiotic agents in bacteria, or the changes wrought in domesticated animals by artificial selection, by presuming that God decided to create organisms in fixed groups, called "kinds" or baramin. Though natural microevolution or human-guided artificial selection can bring about different varieties within the originally created "dog-kind or "cow-kind or "bacteria-kind" !
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The articles provided by kader Asmal of the world Commission on Dams, and water expert Anil Agarwal, seek the path to a new era where social and environmental considerations are given precedence and the benefits of large constructs like dams are questioned. The United States, the second most "dammed" nation, after China, is already breaching many of its dams; elsewhere, particularly in the developing world, the question is how to provide the services supplied by dam projects through other initiatives, like rainwater harvesting and demand management. At the heart of the matter is the value which we assign to different uses of water. Again, there is no universal blueprint, but it is clear that neither of the two extreme stances, one advocating that water should be free for all, and the other promoting full cost pricing for all water supplies, are desirable. We must remember that the value and the price of water are two very different things; it is substance which must be used efficiently, but must be available for the sustenance of all - including natural ecosystems. This makes the pricing of water a tricky business, as we gather further from World Commission on Water Chairman, Ismail Serageldin, and douglas. MacDonalds insights on the subject.
Internal conflicts between ethnic groups, regions, users and small communities can and do arise over water. Inter-state cooperation is essential to the search for regional water solutions. Where such solutions are not easily forthcoming, international mediation and support should be available. A movement to provide such support has been initiated by secretary of State madeleine Albright with the establishment of a global Alliance for Water Security. In most cases, however, the practical solutions required are local, reflecting the geographically and culturally specific nature of water-use. The cold War era of "the bigger the better which prompted the construction of 45,000 large dams throughout the world, is over. This thoughtless tampering with nature has left a terrible egypt legacy, not least in my own region where thousands of acres of fertile land have been lost, and man-made catastrophes such as in the Aral sea region cause immeasurable suffering.
or hidden, in the mainstream peace negotiations. Hanan Sher of The jerusalem Post sheds light on the trials and tribulations encountered on the road towards achieving water for peace in the middle east, a road which I myself have recently revisited. Earlier this year I met with Prime minister Barak, chairman Arafat and King Abdullah of Jordan, and obtained their commitment to work with my organization, Green Cross International, and our partners, the center for Middle east peace and Economic cooperation, to find solutions to the. These three leaders explicitly recognized that there can be no unilateral solutions to their essentially trans-boundary water problems. This is as true in the middle east as it is regarding watercourses shared between the United States and its neighbors. In all of the worlds 261 international basins, joint management should be built on a system of effective interdependence; a pooling rather than a restriction of each nations sovereignty. While armed, inter-state conflicts over water are unlikely, it must be remembered that these are not the only types of conflicts facing water-stressed societies.
There must be plan solidarity in international and regional governance; there must be solidarity between sectors and stakeholders; and there must be political will amongst governments to work in good faith both with their neighbors and with their own people. These people, including often marginalized groups such as women and minorities, must have a voice, and the information and means necessary to use. Without water security, social, economic and national stability are imperiled. This is magnified where water flows across borders - and becomes crucial in regions of religious, territorial or ethnic tension. In some cases, as between India and pakistan over the Indus river, successful cooperation over water resources can be cited as proof that even states with difficult relations can work together. In other cases, the opportunities to improve regional relations which a common watercourse presents have not yet been grasped. The jordan Valley, shared by the people of Israel, palestine, jordan, syria and Lebanon, is one such example.
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Featured Article, thanks to all authors for creating a page that has been read 1,294,745 times. Did this article help you? Water is the most important single element needed in order for people to achieve the universal human right to "a standard of living adequate for the health and well-being of himself and his family." (Article 25, Universal Declaration of Human Rights) Without access to clean. Let us acknowledge that clean water is a universal human right, and in so doing accept that we have the corresponding universal responsibility to ensure that the forecast of a world where, in 25 years' time, two out of every three persons long face water-stress. In this issue, united Nations' secretary general Kofi Annan asks us to face up to the threat of a catastrophic water crisis and counter such bleak forecasts by adopting a new spirit of stewardship. To do otherwise would be nothing less than a crime and history will rightly judge current generations harshly for. The world's growing population should be seen not only as one of the causes of the water crisis, but also as the source of its solution, as is stressed by former President of the Philippines, fidel Ramos, using the example of the enormous potential. Human solidarity is the only force capable of facing a task of this magnitude.