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GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
15 years 1 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
KDD
2008
ACM
140views Data Mining» more  KDD 2008»
15 years 10 months ago
On updates that constrain the features' connections during learning
In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. We invest...
Omid Madani, Jian Huang 0002
IPPS
2003
IEEE
15 years 3 months ago
Constrained Component Deployment in Wide-Area Networks Using AI Planning Techniques
Component-based models represent a dominant trend in the construction of wide-area network applications, making possible the integration of diverse functionality contained in modu...
Tatiana Kichkaylo, Anca-Andreea Ivan, Vijay Karamc...
WWW
2008
ACM
15 years 10 months ago
Algorithm for stochastic multiple-choice knapsack problem and application to keywords bidding
We model budget-constrained keyword bidding in sponsored search auctions as a stochastic multiple-choice knapsack problem (S-MCKP) and design an algorithm to solve S-MCKP and the ...
Yunhong Zhou, Victor Naroditskiy
IPPS
2006
IEEE
15 years 3 months ago
A parallel memetic algorithm applied to the total tardiness machine scheduling problem
This work proposes a parallel memetic algorithm applied to the total tardiness single machine scheduling problem. Classical models of parallel evolutionary algorithms and the gene...
Vinícius Garcia, Paulo M. França, Al...