Examples of using Genetic algorithm in English and their translations into Bulgarian
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We applied the genetic algorithm.
Genetic Algorithm in Optimization.
We can construct a genetic algorithm.
Genetic algorithm is inspired by Darwin's theory about evolution.
He was… he was working with an alloy with a genetic algorithm built into it.
A typical genetic algorithm requires.
Specific crossover made for a specific problem can improve performance of the genetic algorithm.
Mutation(genetic algorithm), an operator in a genetic algorithm of computing.
DM Genetic live wallpaper- This application is a live wallpaper that generates the image by genetic algorithm.
In 1992 John Koza has used genetic algorithm to evolve programs to perform certain tasks.
(Laughter) Now, after five generations of applying evolutionary process, the genetic algorithm is getting a tiny bit better.
By mutating the solutions, a genetic algorithm can reach an improved solution solely through the mutation operator.
Note that after adding and deleting city it is necessary to create new chromosomes andrestart whole genetic algorithm.
Acovea implements a genetic algorithm for finding the"best" options for compiling programs with the GCC C and C++ compilers.
GAJET(Genetic Alrogithm for Java Evolutionary Testing)is an automatic test generation tool for Java that uses a genetic algorithm.
METHODOLOGY Genetic algorithm is a part of evolutionary computing, which is a rapidly growing area of artificial intelligence.
GPdotNET is artificial intelligence tool for applying Genetic Programming and Genetic Algorithm in modeling and optimization of various engineering problems.
As you can see from the genetic algorithm outline, the crossover and mutation are the most important part of the genetic algorithm.
There are many methods, how to find some suitable solution(ie. not necessarily the best solution), for example hill climbing,simulated annealing and genetic algorithm.
Sponsored Links: Acovea implements a genetic algorithm for finding the"best" options for compiling programs with the GCC C and C++ compilers.
Fast Genetic Algorithm is a simple yet powerful implementation of a general genetic algorithm, and provides many types of crossover and selection procedures.
That means that you can user Neural Networks for prediction, buttraining algorithm can be based on Genetic Algorithm or Particle Swarm Optimization or Back Propagation algorithm. .
Each iteration of the genetic algorithm produces a new generation of possible solutions, which, in theory, should be an improvement on the previous generation.
Only by using all three operators together can the genetic algorithm become a noise-tolerant hill-climbing algorithm, yielding good solutions to the problem.
Genetic Algorithm Optimization techniques that use processes such as generic combination, mutation, and natural selection in a design based on the concepts of revolution.
Usually, with a genetic algorithm on a computer today, with a three gigahertz processor, you can solve many formerly intractable problems in just a matter of minutes.
In a genetic algorithm, a population of certain solutions(called individuals) to an optimization problem is evolved toward better solutions.
In a genetic algorithm, a population of candidate solutions(called individuals, creatures, or phenotypes) to an optimization problem is evolved to better solutions.
A typical genetic algorithm requires: a genetic representation of the solution domain, a fitness function to evaluate the solution domain.
The genetic algorithm the team used is derived from nature and contains the phases of population, selection of the most appropriate elements, pairing and mutation of the elements placed in an appropriate environment.