Novelty Search
- method used in genetic algorithms / fitness functions
- instead of trying to optimise for single "best" solution, optimise for "new", previously unseen solutions
- the idea being that evolution is not a product of optimising fitness, but rather a product of open-ended discovery
- I find this interesting when applied to research (and maybe life even?)
- stating the goals too rigidly or too quickly, and then comparing every solution against them (like a fitness function would) can lead to finding non-optimal solutions (also once a solution is found, the search can "stop" - which feels more like engineering-y approach than science-y approach)
The fundamental problem is that the stepping stones that lead to the objective may not resemble the objective itself.
— Novelty Search and the Problem with Objectives
Contrary to intuition, searching without regard to the objective can often outperform searching explicitly for the objective.
— Novelty Search and the Problem with Objectives
natural evolution succeeds because it divergently explores many ways of life while optimizing a behavior (i.e. reproduction) largely orthogonal to what is interesting about its discoveries, while objective-based search directly follows the gradient of improvement until it either succeeds or is too far deceived
— Novelty Search and the Problem with Objectives