Individuals in evolutionary neural networks represent potential solutions, including weights, nodes, and architecture. The most effective systems evolve both parameters and structure, using a DNA-like representation. This approach allows for parallel evaluation across multiple machines, making the process efficient and scalable as each solution's performance can be assessed independently.
