| dc.contributor.author |
Schroder, Ana Julia Oesterreich |
|
| dc.contributor.author |
Moraes, Matheus Bernardelli de |
|
| dc.contributor.author |
Coelho, Guilherme Palermo |
|
| dc.date |
2025-10-24 |
|
| dc.date.accessioned |
2025-06-23 |
|
| dc.date.accessioned |
2025-10-26T01:11:43Z |
|
| dc.date.available |
2025-10-26T01:11:43Z |
|
| dc.identifier.uri |
https://doi.org/10.25824/redu/S4XLON |
|
| dc.identifier.uri |
https://redu.unicamp.br/dataset.xhtml?amp;persistentId=doi:10.25824/redu/S4XLON |
|
| dc.description |
This package contains the utility values used in the 0-1 Knapsack Problem (0-1 KP),
experimental results, and source code related to the paper "Investigating the impacts of different crossover and mutation operators in decomposition-based multi-objective expensive discrete optimization" The following files are included:
<ul>
<li> <i>utility_values.zip</i>: contains the utility values assigned to each item for each objective in the 0-1 KP, organized according to the different problem sizes evaluated. The filenames indicate the objective function and the number of variables. For example, F1_50.csv indicates that the values are used as utility values for objective function 1 considering 50 variables.</li>
<li><i>experiments_results.zip</i>: contains the experimental results for the 0-1 KP benchmark and for the UNISIM-II-D benchmark. The 0-1KP subfolder includes .txt files with the IGD values from each independent run (each line corresponds to one run). The filenames follow the pattern: igd_"crossover_operator"_"mutation_operator"_"size".txt. For example,
igd_uniform_crossover_bitflip_mutation_50.txt contains the IGD values for each of
the 31 runs of MOEA/D using the uniform crossover and bit-flip mutation operators
with 50 variables. The UNISIM_II_D subfolder contains folders with the MOEA/D run results for each operator combination. For instance, in the twopointC_bitflipM folder, files contain iteration-wise results for that operator combination. Additionally, this subfolder includes a non_dominated.xlsx file with the non-dominated solutions obtained for each combination, and a reference_set.csv file containing the values used as the
reference set for plotting the solution frontiers.</li>
<li><i>source_code.zip</i>: contains the source code (in Python) for reproducing the experiments. The code includes: (i) implementations of the MOEA/D algorithms;
(ii) test instances of the 0-1 KP benchmark problems with different dimensionalities
(50, 100, 200, 300, and 400); (iii) reference sets (refsets); (iv) .txt files containing the IGD values of the 31 independent runs for all conducted experiments, named according to the pattern: igd_"crossover_operator"_"mutation_operator"_"size".txt.</li>
</ul>
Usage instructions are provided in the README.txt file included in the package. |
|
| dc.description.sponsorship |
No Funder |
|
| dc.format |
application/zip |
|
| dc.format |
application/zip |
|
| dc.format |
application/zip |
|
| dc.publisher |
Bernardelli de Moraes, Matheus |
|
| dc.subject |
Computer and Information Science |
|
| dc.title |
Replication data for: Investigating the impacts of different crossover and mutation operators in decomposition-based multi-objective expensive discrete optimization |
|
| dc.description.sponsorshipId |
0000 |
|