Variance scaling for EDAs revisited
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Estimation of distribution algorithms (EDAs) are derivative-free optimization approaches based on the successive estimation of the probability density function of the best solutions, and their subsequent sampling. It turns out that the success of EDAs in numerical optimization strongly depends on scaling of the variance. The contribution of this paper is a comparison of various adaptive and self-adaptive variance scaling techniques for a Gaussian EDA. The analysis includes: (1) the Gaussian EDA without scaling, but different selection pressures and population sizes, (2) the variance adaptation technique known as Silverman's rule-of-thumb, (3) σ-self-adaptation known from evolution strategies, and (4) transformation of the solution space by estimation of the Hessian. We discuss the results for the sphere function, and its constrained counterpart.
Original language | English |
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Title of host publication | KI 2011: Advances in Artificial Intelligence : 34th Annual German Conference on AI, Proceedings |
Editors | Joscha Bach, Stefan Edelkamp |
Number of pages | 10 |
Publication date | 2011 |
Pages | 169-178 |
ISBN (Print) | 978-3-642-24454-4 |
ISBN (Electronic) | 978-3-642-24455-1 |
DOIs | |
Publication status | Published - 2011 |
Externally published | Yes |
Event | 34th Annual German Conference on Artificial Intelligence, KI 2011, in Co-location with the 41st Annual Meeting of the Gesellschaft fur Informatik, INFORMATIK 2011 and the 9th German Conference on Multi-Agent System Technologies, MATES 2011 - Berlin, Germany Duration: 4 Oct 2011 → 7 Oct 2011 |
Conference
Conference | 34th Annual German Conference on Artificial Intelligence, KI 2011, in Co-location with the 41st Annual Meeting of the Gesellschaft fur Informatik, INFORMATIK 2011 and the 9th German Conference on Multi-Agent System Technologies, MATES 2011 |
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Land | Germany |
By | Berlin |
Periode | 04/10/2011 → 07/10/2011 |
Series | Lecture notes in computer science |
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Volume | 7006 |
ISSN | 0302-9743 |
ID: 167918534