Szymon Kaliski

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)

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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

Backlinks

1. 2024-04-05Tools for Novelty¹
2. 2023-12-05Leave Room for Errors¹
3. 2022-02-08Play Your Own Games³
4. 2022-02-01Simulator¹
5. 2021-06-28Executing Incomplete Programs and Magic Glass¹