Paper Info
Title
Benchmarking algorithms from the platypus framework on the biobjective bbob-biobj testbed.
Abstract
One of the main goals of the COCO platform is to produce, collect, and make available benchmarking performance data sets of optimization algorithms and, more concretely, algorithm implementations. For the recently proposed biobjective bbob-biobj test suite, less than 20 algorithms have been benchmarked so far but many more are available to the public. We therefore aim in this paper to benchmark several available multiobjective optimization algorithms on the bbob-biobj test suite and discuss their performance. We focus here on algorithms implemented in the platypus framework (in Python) whose main advantage is its ease of use without the need to set up many algorithm parameters.
Year
DOI
Venue
2019
10.1145/3319619.3326896
GECCO
Keywords
Field
DocType
Benchmarking, Black-box optimization, Bi-objective optimization
Test suite,Data set,Computer science,Usability,Algorithm,Testbed,Multi-objective optimization,Implementation,Artificial intelligence,Python (programming language),Benchmarking,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6748-6
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Dimo Brockhoff194853.97
Tea Tusar218119.91