Quantifying and comparing performance of optimization algorithms is one important aspect of research in search and optimization. However, this task turns out to be tedious and difficult to realize even in the single-objective case - at least if one is willing to accomplish it in a scientifically decent and rigorous way. The BBOB 2009 workshop will furnish most of this tedious task for its participants: (1) choice and implementation of a well-motivated real-parameter benchmark function testbed, (2) design of an experimental set-up, (3) generation of data output for (4) post-processing and presentation of the results in graphs and tables. What remains to be done for the participants is to allocate CPU-time, run their favorite black-box real-p...
International audienceExisting studies in black-box optimization for machine learning suffer from lo...
This document presents the results from the BBOB Black-Box Optimization Benchmarking workshop of the...
This paper presents results of the BBOB-2009 benchmark-ing of 31 search algorithms on 24 noiseless f...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
This document presents the results in the form of tables from the Black-Box Optimization Benchmarkin...
This document presents the results from the BBOB Black-Box Optimization Benchmarking workshop of the...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
pp. 1689-1696This paper presents results of the BBOB-2009 benchmark- ing of 31 search algorithms on ...
International audienceWe benchmark the Pure-Random-Search algorithm on the BBOB 2009 noisy testbed. ...
In this work we evaluate a Particle Swarm Optimizer hy- bridized with Di®erential Evolution and app...
International audienceThe NEWUOA which belongs to the class of Derivative-Free optimization algorith...
This document presents the results in the form of tables from the Black-Box Optimization Benchmarkin...
International audienceExisting studies in black-box optimization for machine learning suffer from lo...
This document presents the results from the BBOB Black-Box Optimization Benchmarking workshop of the...
This paper presents results of the BBOB-2009 benchmark-ing of 31 search algorithms on 24 noiseless f...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
This document presents the results in the form of tables from the Black-Box Optimization Benchmarkin...
This document presents the results from the BBOB Black-Box Optimization Benchmarking workshop of the...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
pp. 1689-1696This paper presents results of the BBOB-2009 benchmark- ing of 31 search algorithms on ...
International audienceWe benchmark the Pure-Random-Search algorithm on the BBOB 2009 noisy testbed. ...
In this work we evaluate a Particle Swarm Optimizer hy- bridized with Di®erential Evolution and app...
International audienceThe NEWUOA which belongs to the class of Derivative-Free optimization algorith...
This document presents the results in the form of tables from the Black-Box Optimization Benchmarkin...
International audienceExisting studies in black-box optimization for machine learning suffer from lo...
This document presents the results from the BBOB Black-Box Optimization Benchmarking workshop of the...
This paper presents results of the BBOB-2009 benchmark-ing of 31 search algorithms on 24 noiseless f...