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Still a flash point among fundamentalist Christians, the theory of evolution proposed by Charles Darwin 150 years ago has become an indispensable tool for biologists to comprehend the natural world.
Yet as scientists mark Darwin’s 200th birthday this month, evolution is no longer simply a narrative of life. Scientists have begun using it as a tool to develop new technologies.
By doing so, they have improved law enforcement, created smarter computer programs and are remaking the field of medicine. There have been quirkier applications, such as cleaner clothes, too.
But Andy Ellington, a University of Texas evolutionary biologist, called that argument “almost amusing.”
“You have these folks who are trying to suggest that we shouldn’t teach evolution as something our kids need to know,” he said. “But at the same time, there are these new technologies out there shaping our lives every day.”
Originally posted by Lasheic
I don't know what the creationist response to these are, because I've never gotten one to give me a coherent rebuttal to their usefulness or effectiveness.
Originally posted by johnsky
To other members who have also studied the theory, sorry for the layman's talk... I just don't want to exclude those who haven't educated themselves on the topic.
Originally posted by Lasheic
I don't know what the creationist response to these are, because I've never gotten one to give me a coherent rebuttal to their usefulness or effectiveness.
New Scientist - EA's now surpass human designers.
Originally posted by golemina
It is so rampant in every field that the methodology of Science is practically nonexistant.
Only the most skilled and honest of observers will even admit that the rampant adherence to the paradigm basically makes 'scientists' blind to ANY evidence to the contrary.
The real elephant in the room is the role of vested interests. It has diminished Science to 'science'... an absolute farce.
Originally posted by golemina
It was, of course, assumed we would take the exchange somewhere else.
While I don't think evolution accoutns for the origins of life,
His internationally recognized research program in comparative physiology and biomechanics has shown how examining a diversity of animals leads to the discovery of general principles of locomotion... At the same time, discovering the function of simple, tractable neuromechanical systems along with a knowledge of evolution can provide new design ideas applicable to the control of animal and human gait. Recently, Professor Full's research has focused on the role of the mechanical system in self-stabilization.
In computer science, evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that involves combinatorial optimization problems.
Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution.
A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.
Methodology
Genetic algorithms are implemented in a computer simulation in which a population of abstract representations (called chromosomes or the genotype of the genome) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached.
Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics and other fields.
Your trying to state that the whims of public opinion are polluting the results...
could you two clear that up for me please otherwise I have to agree with her statement