Examples of using Genetic algorithms in English and their translations into Ukrainian
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Genetic Algorithms for Optimization.
The First International Conference on Genetic Algorithms.
In machine learning, genetic algorithms were used in the 1980s and 1990s.
An alternative approach might be to use genetic algorithms.
Genetic algorithms are promising at searching in large and complex search spaces.
This diversification scheme is an important characteristic of genetic algorithms.
Genetic algorithms are different from more normal optimization and search procedures in four ways:.
Charley Davenport- A member of Jack's team who specializes in genetic algorithms.
In this case, genetic algorithms frequently become the most appropriate method for searching for"good" values.
Therein, he introduced the Turing test, machine learning, genetic algorithms, and reinforcement learning.
Goldberg[6] stated that genetic algorithms are different from other optimization and search procedures in the following aspects:.
The most common globaloptimization method for training RNNs is genetic algorithms, especially in unstructured networks.
Genetic algorithms are great for certain things; I suspect I know what they're bad at, and I won't tell you.
In 1975 he wrote the ground-breaking book on genetic algorithms,"Adaptation in Natural and Artificial Systems".
Genetic algorithms- adaptive search methods that have been commonly used for solving tasks of functional optimization lately.
Area in computer science that uses genetic algorithms is sometimes confused with computer evolutionary biology.
Successful verification occurs in all search-based AI systems, such as planners, game-players,even genetic algorithms.
Area in computer science that uses genetic algorithms is sometimes confused with computer evolutionary biology.
To analyze the data of spectrophotometric titration we built a new system of multi-threaded paralleloptimization of the absorption spectra of BAS using genetic algorithms.
The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology.
Nevertheless, genetic algorithms are among standard modern instruments for data mining, and thus they are included in the present overview.
The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology.
By mimicking this process, genetic algorithms are able to"evolve" solutions to real world problems, if they have been suitably encoded.
Research in GAs remained largely theoretical until the mid-1980s,when The First International Conference on Genetic Algorithms was held in Pittsburgh, Pennsylvania.
By imitating this process, genetic algorithms are capable of evolving solutions of real tasks, if they are coded appropriately.
In new developments for construction of models from data, evolution and genetic algorithms, also ideas of active neurons and multilevel selforganization and others are used.
The NASA Advanced Supercomputing facility(NAS) ran genetic algorithms using the Condor cycle scavenger running on about 350 Sun Microsystems and SGI workstations.
Principles of hybridizationof sorting-out GMDH and genetic algorithms, based on which a searching algorithm COMBI-GA has been constructed[11].
In particular, in the fields of genetic programming and genetic algorithms, each design solution is commonly represented as a string of numbers(referred to as a chromosome).