AI/ML

The Neighbours' Similar Fitness Property for Local Search. (arXiv:2001.02872v1 [cs.AI])



For most practical optimisation problems local search outperforms random
sampling – despite the “No Free Lunch Theorem”. This paper introduces a
property of search landscapes termed Neighbours’ Similar Fitness (NSF) that
underlies the good performance of neighbourhood search in terms of local
improvement. Though necessary, NSF is not sufficient to ensure that searching
for improvement among the neighbours of a good solution is better than random
search. The paper introduces an additional (natural) property which supports a
general proof that, for NSF landscapes, neighbourhood search beats random
search.

Source link




Related posts

Building A Twitter Bot

Newsemia

[SSCAIT] StarCraft Artificial Intelligence Tournament Live Stream

Newsemia

IEEE Transactions on Neural Networks and Learning Systems, Volume 29, Issue 6, June 2018

Newsemia

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy