Examples of using Genetic algorithms in English and their translations into Bulgarian
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Introduction to Genetic Algorithms.
Genetic Algorithms for Optimization.
Evolutionary and Genetic Algorithms.
Genetic algorithms and artificial life.
Neural nets and genetic algorithms.
Genetic Algorithms are based on Darwin's Theory of Evolution.
Neural networks and genetic algorithms.
What are genetic algorithms and why do we need them?
Neural networks, metaheuristics and genetic algorithms.
Combinations of genetic algorithms and neural networks.
For that we used artificial evolution-- genetic algorithms.
Genetic algorithms are inspired by Darwin's theory about evolution.
Solution to a problem solved by genetic algorithms is evolved.
An overview of genetic algorithms for the solution of optimisation problems.
These pages introduce some fundamentals of genetic algorithms.
Incorporating genetic algorithms to sell near the top and buy near the bottom.
Simply said, solution to a problem solved by genetic algorithms is evolved.
Basic Description Genetic algorithms are inspired by Darwin's theory about evolution.
Of course, inexpensive microprocessors and a very important breakthrough-- genetic algorithms.
And we're learning from neural nets, genetic algorithms, evolutionary computing.
Genetic Algorithms were used them to optimize the topology of a neural network.
You can find here several interactive Java applets demonstrating work of genetic algorithms.
In machine learning, genetic algorithms were used in the 1980s and 1990s.
Rather then standard algorithms, parallel neural networks and genetic algorithms shall be massively used.
In machine learning, genetic algorithms found some uses in the 1980s and 1990s.
As opposed to customary algorithms, parallel neural networks and genetic algorithms are massively chosen.
Genetic algorithms are search algorithms based on the mechanics of natural.
And evolutionary algorithms such as genetic algorithms and genetic programming.
Genetic algorithms are primarily used to optimize neural network topologies and weights.
Evolution and Genetic Computation(e.g., genetic algorithms, genetic programming).