Variance scaling for EDAs revisited
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Variance scaling for EDAs revisited. / Kramer, Oliver; Gieseke, Fabian.
KI 2011: Advances in Artificial Intelligence: 34th Annual German Conference on AI, Proceedings. red. / Joscha Bach; Stefan Edelkamp. 2011. s. 169-178 (Lecture notes in computer science, Bind 7006).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Variance scaling for EDAs revisited
AU - Kramer, Oliver
AU - Gieseke, Fabian
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80053974567&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24455-1_16
DO - 10.1007/978-3-642-24455-1_16
M3 - Article in proceedings
AN - SCOPUS:80053974567
SN - 978-3-642-24454-4
T3 - Lecture notes in computer science
SP - 169
EP - 178
BT - KI 2011: Advances in Artificial Intelligence
A2 - Bach, Joscha
A2 - Edelkamp, Stefan
T2 - 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
Y2 - 4 October 2011 through 7 October 2011
ER -
ID: 167918534