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» On the Evolution of Evolutionary Algorithms
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JACM
2006
99views more  JACM 2006»
14 years 9 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller
BMCBI
2005
201views more  BMCBI 2005»
14 years 9 months ago
Principal component analysis for predicting transcription-factor binding motifs from array-derived data
Background: The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to...
Yunlong Liu, Matthew P. Vincenti, Hiroki Yokota
ATAL
2007
Springer
15 years 4 months ago
Batch reinforcement learning in a complex domain
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
Shivaram Kalyanakrishnan, Peter Stone
GECCO
2005
Springer
186views Optimization» more  GECCO 2005»
15 years 3 months ago
Subproblem optimization by gene correlation with singular value decomposition
Several ways of using singular value decomposition (SVD), a linear algebra technique typically used for information retrieval, to decompose problems into subproblems are investiga...
Jacob G. Martin
GECCO
2010
Springer
194views Optimization» more  GECCO 2010»
15 years 1 months ago
Mirrored variants of the (1, 2)-CMA-ES compared on the noisy BBOB-2010 testbed
Derandomization by means of mirrored samples has been recently introduced to enhance the performances of (1, λ) and (1 + 2) Evolution-Strategies (ESs) with the aim of designing f...
Anne Auger, Dimo Brockhoff, Nikolaus Hansen