Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Markov Chain Analysis of Noise and Restart in Stochastic Local Search
Authors: Ole J. Mengshoel, Youssef Ahres, Tong Yu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments with synthetic and real-world problems, we compare Soft SLS, Adaptive SLS, Adaptive Noise [Hoos, 2002], and Simulated Annealing [Kirkpatrick et al., 1983]. Table 2: Average running time results for optimized Soft SLS, Adaptive SLS, Adaptive Noise (AN), and Simulated Annealing (SA) on synthetic DMC problems. |
| Researcher Affiliation | Academia | Ole J. Mengshoel Electrical and Computer Engineering Carnegie Mellon University, Youssef Ahres Electrical Engineering Stanford University, Tong Yu Electrical and Computer Engineering Carnegie Mellon University |
| Pseudocode | Yes | Algorithm 1: Markov SLS is an algorithm where noise probability pn and restart probability pr can be adapted. |
| Open Source Code | No | No explicit statement or link for open-source code availability found. |
| Open Datasets | Yes | We use both synthetic (deceptive Markov chains) and realworld datasets (feature selection) in our experiments. Table 1 presents the real-world datasets used for feature selection. Please see https://archive.ics.uci.edu/ml and http://www.liacc. up.pt |
| Dataset Splits | Yes | its cross-validation accuracy, using 2-fold cross-validation. |
| Hardware Specification | No | No specific hardware details are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | Yes | In this paper, their optimized values are: an = 0.2 and ar = 0.1. For each dataset, we searched exhaustively to predetermine the globally optimal feature subset s , and passed t = f(s ) to both Soft SLS and Adaptive SLS. its cross-validation accuracy, using 2-fold cross-validation. Figure 4 shows the evolution of pr and pn in Adaptive SLS during search, after initialization pr = pn = 0. |