Population-based incremental learning

Population-based incremental learning is like a big class of robots that are trying to learn how to do something. Imagine you are in a big classroom with lots of other kids. Everyone in the class is trying to learn math, but some people might be better at addition and others at multiplication.

Similarly, in population-based incremental learning, there are lots of "robots" that are all trying to learn how to do something, like finding the best route between two points. Each robot is different and has its own way of finding the best route. Some robots might be good at finding shortcuts, while others might be better at avoiding traffic.

The robots learn by working in a team. They share their knowledge and adapt to changes in their environment. If one robot finds a better route, it teaches the others so they can all get better.

Just like in a classroom, the robots are graded based on their performance. The robots that perform the best are kept while the ones that don't do as well are discarded. This process is repeated over and over again, with new generations of robots being created each time. As the generations progress, the robots become more and more skilled at finding the best route.

So, in summary, population-based incremental learning is like a class of robots working together to learn and improve, with the best robots being kept and the weaker ones being discarded to create a new generation of robots.