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ICML
2004
IEEE
16 years 18 days ago
Convergence of synchronous reinforcement learning with linear function approximation
Synchronous reinforcement learning (RL) algorithms with linear function approximation are representable as inhomogeneous matrix iterations of a special form (Schoknecht & Merk...
Artur Merke, Ralf Schoknecht
GECCO
2008
Springer
152views Optimization» more  GECCO 2008»
15 years 27 days ago
Designing EDAs by using the elitist convergent EDA concept and the boltzmann distribution
This paper presents a theoretical definition for designing EDAs called Elitist Convergent Estimation of Distribution Algorithm (ECEDA), and a practical implementation: the Boltzm...
Sergio Ivvan Valdez Peña, Arturo Hern&aacut...
JMLR
2011
133views more  JMLR 2011»
14 years 6 months ago
Operator Norm Convergence of Spectral Clustering on Level Sets
Following Hartigan (1975), a cluster is defined as a connected component of the t-level set of the underlying density, that is, the set of points for which the density is greater...
Bruno Pelletier, Pierre Pudlo
GECCO
2010
Springer
148views Optimization» more  GECCO 2010»
15 years 4 months ago
Guarding against premature convergence while accelerating evolutionary search
The fundamental dichotomy in evolutionary algorithms is that between exploration and exploitation. Recently, several algorithms [8, 9, 14, 16, 17, 20] have been introduced that gu...
Josh C. Bongard, Gregory S. Hornby
EC
2002
180views ECommerce» more  EC 2002»
14 years 11 months ago
Combining Convergence and Diversity in Evolutionary Multiobjective Optimization
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to nd a number of ...
Marco Laumanns, Lothar Thiele, Kalyanmoy Deb, Ecka...