A Support-Based Algorithm for the Bi-Objective Pareto Constraint
Authors: Renaud Hartert, Pierre Schaus
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | 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 {renaud.hartert, pierre.schaus}@uclouvain.be |
| 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. |