Local Optima Networks of NK Landscapes with Neutrality
S\'ebastien Verel (INRIA Lille - Nord Europe), Gabriela Ochoa, Marco, Tomassini (ISI)

TL;DR
This paper extends local optima network models to neutral NK landscapes, revealing how neutrality influences landscape structure and potentially enhances heuristic search, despite requiring exhaustive enumeration.
Contribution
It introduces a formalism for local optima networks in neutral landscapes and demonstrates their properties through empirical analysis of NKp and NKq models.
Findings
Neutral landscapes' features interpolate between maximum neutrality and non-neutral cases.
Structural differences exist between NKp and NKq neutral landscape families.
Neutrality may improve heuristic search effectiveness in landscapes with percolating neutral networks.
Abstract
In previous work we have introduced a network-based model that abstracts many details of the underlying landscape and compresses the landscape information into a weighted, oriented graph which we call the local optima network. The vertices of this graph are the local optima of the given fitness landscape, while the arcs are transition probabilities between local optima basins. Here we extend this formalism to neutral fitness landscapes, which are common in difficult combinatorial search spaces. By using two known neutral variants of the NK family (i.e. NKp and NKq) in which the amount of neutrality can be tuned by a parameter, we show that our new definitions of the optima networks and the associated basins are consistent with the previous definitions for the non-neutral case. Moreover, our empirical study and statistical analysis show that the features of neutral landscapes interpolate…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Artificial Immune Systems Applications · Diffusion and Search Dynamics
