Back to Search
Start Over
Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed
- Source :
- Genetic and Evolutionary Computation Conference (GECCO 2012) (2012)
- Publication Year :
- 2012
-
Abstract
- In this paper, we study the performance of IPOP-saACM-ES and BIPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategies. Both algorithms were tested using restarts till a total number of function evaluations of $10^6D$ was reached, where $D$ is the dimension of the function search space. We compared surrogate-assisted algorithms with their surrogate-less versions IPOP-saACM-ES and BIPOP-saACM-ES, two algorithms with one of the best overall performance observed during the BBOB-2009 and BBOB-2010. The comparison shows that the surrogate-assisted versions outperform the original CMA-ES algorithms by a factor from 2 to 4 on 8 out of 24 noiseless benchmark problems, showing the best results among all algorithms of the BBOB-2009 and BBOB-2010 on Ellipsoid, Discus, Bent Cigar, Sharp Ridge and Sum of different powers functions.<br />Comment: arXiv admin note: substantial text overlap with arXiv:1206.0974
- Subjects :
- Computer Science - Neural and Evolutionary Computing
Subjects
Details
- Database :
- arXiv
- Journal :
- Genetic and Evolutionary Computation Conference (GECCO 2012) (2012)
- Publication Type :
- Report
- Accession number :
- edsarx.1206.5780
- Document Type :
- Working Paper