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 please click for source such as HIV - is a straightforward consequence of the laws of mutation and selection, and understanding these principles has helped us to craft strategies for dealing with these harmful organisms.
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 yearsbut less well-known examples include cultivated maize very different from its wild relatives, none of which have the familiar "ears" of human-grown corngoldfish like dogs, we have bred varieties that look dramatically different from the wild typeand dairy cows with immense udders far larger than would be required just for nourishing offspring.
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 Buy Astronomy Dissertation Hypothesis within the originally created "dog-kind," or "cow-kind," or read article 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 Buy Astronomy Dissertation Hypothesis as well.
This field is computer science, and the benefits come from a programming strategy called genetic algorithms. 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 Buy Astronomy Dissertation Hypothesis 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!
Before a genetic algorithm can be put Buy Astronomy Dissertation Hypothesis visit web page 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 Buy Astronomy Dissertation Hypothesis and complexity than the comparatively restricted method of using binary numbers only Buy Astronomy Dissertation Hypothesis 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 Buy Astronomy Dissertation Hypothesis 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 source 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 Buy Astronomy Dissertation Hypothesis technique is Hiroaki Kitano's "grammatical read article 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.
The Church-Turing Thesis
Another strategy, developed principally by John Koza of Stanford University and called genetic programmingrepresents programs as branching data structures called trees Koza et al. In this approach, random changes can be Buy Astronomy Dissertation Hypothesis 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 check this out to solve whatever problem they are applied to.
There are many different techniques which a genetic algorithm http://cocktail24.info/blog/esl-persuasive-essay-editor-site-au.php use to select the individuals to be Buy Astronomy Dissertation Hypothesis 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. More fit individuals are more likely, but not certain, to be selected. 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.
As the average fitness of the population Buy Astronomy Dissertation Hypothesis, the strength of the selective pressure also increases and the fitness function becomes more discriminating. Buy Astronomy Dissertation Hypothesis 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. Subgroups of individuals are chosen from the larger population, and members of each subgroup compete against each other.
Only one Buy Astronomy Dissertation Hypothesis from each subgroup is chosen to reproduce. 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. The offspring of the individuals selected from each generation become the Buy Astronomy Dissertation Hypothesis next generation.
No individuals are retained between generations. 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 read more generation.
Some individuals are retained between generations. Individuals go through multiple rounds of selection each generation. Lower-level evaluations are faster and less discriminating, while those that survive to higher levels are evaluated more rigorously. The advantage of this method is that it reduces overall computation time by using faster, less selective evaluation to weed out the majority of individuals that show little or no promise, and only subjecting those who survive this initial test to more rigorous and more computationally expensive fitness evaluation.
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Once go here has chosen fit individuals, they must be randomly altered in hopes of improving their fitness for the next generation. There are two basic strategies to accomplish this.
The first and simplest is called mutation. Just as mutation in living things changes one gene to another, so mutation in a genetic algorithm causes small alterations at single points in an individual's code. The second method Buy Astronomy Dissertation Hypothesis called crossoverand entails choosing two individuals to swap segments of their code, producing artificial "offspring" that are combinations of their parents.
This process is intended to simulate the analogous process of recombination that occurs to chromosomes during sexual reproduction. The above diagrams illustrate the effect of each of these genetic operators on individuals in a population of 8-bit strings. The upper diagram shows two individuals undergoing single-point crossover; the point of exchange is set between the fifth and sixth positions in the genome, producing a new individual that is a hybrid of its progenitors.
The second diagram shows an individual undergoing mutation at position 4, changing the 0 at that position in its genome to a 1.
With the rise of artificial life computing and the development of heuristic methods, other computerized problem-solving techniques have emerged that are in some ways similar to genetic algorithms. This section explains some of these techniques, in what ways they resemble GAs and in what ways they differ. A simple feedforward neural network, with one input layer consisting of four neurons, one hidden layer consisting of three neurons, and one output layer consisting of four neurons.
The number on each neuron represents its activation threshold: The diagram shows the neural network being presented with an input string and shows how activation spreads forward through the network to produce an output. The earliest instances of what might today be called genetic algorithms appeared in the late s and early s, programmed on computers by evolutionary biologists who were explicitly seeking to model aspects of natural evolution.
It did not occur to any of them that this strategy might be more generally applicable to artificial problems, but that recognition was not long in coming: Byresearchers such as G.
Bremermann had all independently developed evolution-inspired algorithms for function optimization and machine learning, but their work attracted Buy Astronomy Dissertation Hypothesis followup. A more successful development in this area came inwhen Ingo Rechenberg, then of the Technical University of Berlin, introduced a technique he called evolution strategyplease click for source it was more similar to hill-climbers than to genetic algorithms.
In this technique, there was no population or crossover; one parent was mutated to produce one offspring, and the better of the two was kept and became the parent for the next round of mutation Haupt and Hauptp. Later versions introduced the idea of a population. Evolution strategies are still Buy Astronomy Dissertation Hypothesis today by engineers and scientists, especially in Germany. The next important development in the field came inwhen L. Walsh introduced in America a technique they source evolutionary programming.
In this method, candidate solutions to problems were represented as simple finite-state machines; like Rechenberg's evolution strategy, their algorithm worked by randomly mutating one of these simulated machines and keeping the better of the two Mitchellp.
Also like evolution strategies, a broader formulation of the evolutionary programming technique is still an area of ongoing research today. However, what was still lacking in both these methodologies was recognition of the importance of crossover. As early asJohn Holland's work on adaptive systems laid the foundation for later developments; most notably, Holland was also the first to explicitly propose crossover and other recombination operators.
However, the Buy Astronomy Dissertation Hypothesis work in the field of genetic algorithms came inwith the publication of the book Adaptation in Natural and Artificial Systems.