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..
A Support-Based Algorithm for the Bi-Objective Pareto Constraint
Authors: Renaud Hartert, Pierre Schaus
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficiency of this algorithm is experimentally confirmed on classical bi-objective benchmarks.Section five directly follows with our experiments and results on two classical benchmarks i.e. the bi-objective knapsack problem and the bi-objective travelling salesman problem. |
| Researcher Affiliation | Academia | Renaud Hartert and Pierre Schaus UCLouvain, ICTEAM, Place Sainte Barbe 2, 1348 Louvain-la-Neuve, Belgium EMAIL |
| Pseudocode | No | The paper describes algorithms verbally but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'All algorithms were implemented in the open-source Osca R solver (Osca R Team 2012)' but does not provide concrete access to the specific source code of the methodology described in this paper. |
| Open Datasets | Yes | Our experiments used classical instances of the bi-objective binary knapsack problem (Xavier Gandibleux 2013) and instances of the bi-objective traveling salesman problem (Paquete and St utzle 2009). |
| Dataset Splits | No | The paper mentions 'instances of the bi-objective binary knapsack problem' and 'instances of the bi-objective traveling salesman problem' but does not specify dataset splits (e.g., train/validation/test percentages or counts) for reproduction. |
| Hardware Specification | Yes | All algorithms were implemented in the open-source Osca R solver (Osca R Team 2012) that runs on the Java Virtual Machine using a computer running Mac Os X 10.9 on an Intel i7 2.6 Ghz processor. |
| Software Dependencies | Yes | All algorithms were implemented in the open-source Osca R solver (Osca R Team 2012). |
| Experiment Setup | Yes | All algorithms were implemented in the open-source Osca R solver (Osca R Team 2012) that runs on the Java Virtual Machine using a computer running Mac Os X 10.9 on an Intel i7 2.6 Ghz processor. First, we compare the number of search nodes explored using both algorithms on instances of the bi-objective binary knapsack problem within a time limit of 30 seconds. In this experiment, the bi-objective Pareto constraint starts with an empty archive and explores the search-space with a random heuristic.In this experiments, the Pareto constraint starts with an initial archive that is a good approximation of the exact efficient set. |