GANN Meaning and
Definition
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GANN (Genetic Algorithm Neural Network) is a computational technique used in the field of artificial intelligence and machine learning. It combines elements of genetic algorithms, which mimic natural selection and evolution, with neural networks, which are computing systems inspired by the structure and functions of biological brains.
In GANN, the neural network is trained using a genetic algorithm. It starts with a population of randomly initialized neural networks, known as individuals or chromosomes, and they are subjected to a process of evolution and natural selection. This process involves evaluating the performance of each individual, selecting the fittest ones, applying genetic operators such as mutation and crossover to generate new individuals, and repeating this cycle over multiple generations.
The goal of GANN is to optimize the neural network's architecture and parameters to achieve the best possible performance on a given task or problem. It leverages the strength of genetic algorithms in exploring a large search space and dynamically adapting the structure of the neural network for better generalization and efficiency.
GANN has been successfully applied to various problems in different domains, such as image and speech recognition, financial forecasting, and game playing. Its ability to automatically tune neural networks and discover optimal configurations makes it a powerful tool in the field of artificial intelligence, enabling systems to learn and improve their performance without the need for manual intervention.
Common Misspellings for GANN
- gfann
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Similar spelling words for GANN
- game,
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