Examples of using Genetic algorithms in English and their translations into Hungarian
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Simple Genetic Algorithms.
Artificial Intelligence: Machine learning algorithms(decision trees, genetic algorithms, neuron networks).
Topics: Genetic Algorithms.
Of course,inexpensive microprocessors and a very important breakthrough-- genetic algorithms.
How Genetic Algorithms Work?
Evolve biomorphs using genetic algorithms.
How do genetic algorithms work?
Therein, he introduced the Turing test, machine learning, genetic algorithms, and reinforcement learning.
Genetic Algorithms and Evolutionary Computation.
Fuzzy Sets, Genetic Algorithms.
Genetic algorithms have been proposed to solve this problem.
He was talking about genetic algorithms, quantum teleportation.
Genetic algorithms are effective in solving these kinds of problems.
In my thesis I examined the genetic algorithms' structure and behaviour.
Genetic algorithms are a kind of artificial intelligence.
And after very simple neural network-- genetic algorithms and so on-- look at the pattern.
The genetic algorithms perform so well because of their adaptability.
These methods most notably divide into evolutionary algorithms(e.g. genetic algorithms) and swarm intelligence(e.g. ant algorithms). .
More recently, genetic algorithms are used to evolve the neural structure.
The programme encompasses algorithms analysis, network programming, distributed systems, databases, internet portals, artificial intelligence,neural networks and genetic algorithms.
All fuzzy logic, genetic algorithms and neural network design.
Creationists are making a strong case that genomes are not static and that the DNA sequence can change over time,but they are also stating that some of these changes are controlled by genetic algorithms built into the genomes themselves.
Genetic algorithms in particular became popular through the writing of John Henry Holland.
The NASA Advanced Supercomputing facility(NAS) has run genetic algorithms using the Condor cycle scavenger running on about 350 Sun and SGI workstations.
Genetic algorithms Genetic algorithms(GA) are an optimization methodology based on a direct analogy to Darwinian natural selection and mutations in biological reproduction.
Over the past few decades, evolutionary computing methods, especially genetic algorithms are used countless times for analytically difficult or intractable optimization problems to approximate a globally optimal solution.
As a senior student, you may find yourself assisting in areas of research such as natural language processing, speech perception, cognitive engineering, formal properties of grammars, modeling and simulation of games, human attention and performance, philosophy of mind, development of children's thinking,semantics or genetic algorithms.
A possible alternative to this route is to use perhaps genetic algorithms and some methods from classical AI to create a superintelligences that may not bear a close resemblance to human brains.
We can use genetic algorithms to provide high-quality solutions to this problem, at a much lower cost than the more primitive problem-solving techniques, like exhaustive search, which would require you to permute through all possible solutions.
Solver has an improved user interface, a new Evolutionary Solver, based on genetic algorithms, that handles models with any Excel functions, new global optimization options, better linear programming and nonlinear optimization methods, and new Linearity and Feasibility reports.