Evolving Neural Networks: Merging Learning and Evolutionary Strategies

This exploration of evolving neural networks focuses on two primary forms of adaptation: learning and evolution. Learning involves training on example datasets to minimize errors and fit behavior to the provided data. In contrast, evolutionary approaches use population-based searches, random mutations, reproduction, and fitness selection to optimize neural networks. This work highlights the synergy of combining these two adaptation strategies to enhance neural network performance, paving the way for more robust and efficient AI models.

• neural networks
• adaptation strategies
• evolutionary algorithms
• machine learning text

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