Neuroevolution

Neuroevolution is a method of designing artificial neural networks by simulating natural evolution. Instead of manually programming the network, multiple versions are created, evaluated based on their performance, and then the best are combined and slightly altered (mutated) to produce new versions. Over many generations, this process fine-tunes the network's structure and parameters, leading to effective solutions for complex problems. It mimics biological evolution to discover circuits that can learn, adapt, or make decisions in tasks like game playing or robotic control without explicit programming of every detail.