Understanding the role of noise in stochastic local search: Analysis and experiments

Stochastic local search (SLS) algorithms have recently been proven to be among the best approaches to solving computationally hard problems. SLS algorithms typically have a number of parameters, optimized empirically, that characterize and determine their performance. In this article, we focus on the noise parameter. The theoretical foundation of SLS, including an understanding of how to the optimal noise varies with problem di

• Type: Journal
• Year: 2008
• Where: AI
• Authors: Ole J. Mengshoel
• Keywords: AI 2008, Artificial Intelligence, Problem Di¢ Culty, Problem Instances, Stochastic Local Search