Adaptive Landscape
Updated 2026-09-11
INTRODUCTION
English translation pending.
CORE DEFINITION
The adaptive landscape, modeled by Stuart Kauffman with the NK model, locates species on a fitness surface where peaks represent well-adapted configurations. The number of interactions K between gene sites determines how rugged the landscape is. When K is small the landscape is smooth and easy to search; when K is large the surface becomes rugged, packed with local optima that trap any hill-climbing search.
SCAFFOLDING EFFECT
Reduce cognitive load
- Gauge interdependence: estimate how strongly the system's components interact before searching. - Read the terrain: classify the landscape as smooth or rugged and set expectations accordingly. - Escape local peaks: recognize when no small move can improve the position and a jump is required.
Anchor fast decisions
In the NK model each gene site's fitness contribution depends on K other sites. With high K, changing one part shifts many others, so the surface becomes corrugated with local optima, and a hill-climbing search stalls at the nearest peak because every small step leads downhill. Interdependence, not the size of the space alone, is what makes the optimization hard.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Fitness_landscapeverified
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