Which statement best describes a difference between neural networks and genetic algorithms?

Neural networks are designed to mimic the way the human brain operates, enabling them to learn from data by recognizing patterns and making predictions. They achieve this through a process called training, where the network adjusts its internal parameters based on the data it processes. This learning capability allows neural networks to improve their performance over time as they are exposed to more information, making option C a strong descriptor of their functionality.

In contrast, genetic algorithms operate based on the principles of natural selection and evolution. They are population-based optimization techniques that iteratively evolve a set of solutions towards better performance, rather than learning from data in the same way neural networks do. This fundamental difference in approach highlights why option C accurately captures the distinguishing feature of neural networks, as it emphasizes their ability to 'learn' from the data provided.