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» On Genetic Algorithms and Lindenmayer Systems
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BMCBI
2008
160views more  BMCBI 2008»
14 years 11 months ago
Feature selection environment for genomic applications
Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e....
Fabrício Martins Lopes, David Correa Martin...
VLSID
2007
IEEE
206views VLSI» more  VLSID 2007»
16 years 2 days ago
MAX: A Multi Objective Memory Architecture eXploration Framework for Embedded Systems-on-Chip
Today's feature-rich multimedia products require embedded system solution with complex System-on-Chip (SoC) to meet market expectations of high performance at a low cost and l...
T. S. Rajesh Kumar, C. P. Ravikumar, R. Govindaraj...
HPDC
2006
IEEE
15 years 5 months ago
Scheduling Mixed Workloads in Multi-grids: The Grid Execution Hierarchy
Consider a workload in which massively parallel tasks that require large resource pools are interleaved with short tasks that require fast response but consume fewer resources. We...
Mark Silberstein, Dan Geiger, Assaf Schuster, Miro...
GECCO
2003
Springer
112views Optimization» more  GECCO 2003»
15 years 4 months ago
Limits in Long Path Learning with XCS
The development of the XCS Learning Classifier System [26] has produced a stable implementation, able to consistently identify the accurate and optimally general population of cla...
Alwyn Barry
ICDE
2009
IEEE
156views Database» more  ICDE 2009»
15 years 6 months ago
Support Multi-version Applications in SaaS via Progressive Schema Evolution
— Update of applications in SaaS is expected to be a continuous efforts and cannot be done overnight or over the weekend. In such migration efforts, users are trained and shifted...
Jianfeng Yan, Bo Zhang