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GECCO
2006
Springer
166views Optimization» more  GECCO 2006»
15 years 10 months ago
Comparing genetic robustness in generational vs. steady state evolutionary algorithms
Previous research has shown that evolutionary systems not only try to develop solutions that satisfy a fitness requirement, but indirectly attempt to develop genetically robust so...
Josh Jones, Terry Soule
FPL
2004
Springer
144views Hardware» more  FPL 2004»
15 years 10 months ago
A Methodology for Energy Efficient FPGA Designs Using Malleable Algorithms
A recent trend towards integrating FPGAs with many heterogeneous components, such as memory systems, dedicated multipliers, etc., has made them an attractive option for implementin...
Jingzhao Ou, Viktor K. Prasanna
IPPS
1997
IEEE
15 years 10 months ago
Designing Efficient Distributed Algorithms Using Sampling Techniques
In this paper we show the power of sampling techniques in designing efficient distributed algorithms. In particular, we show that using sampling techniques, on some networks, sele...
Sanguthevar Rajasekaran, David S. L. Wei
GECCO
2010
Springer
244views Optimization» more  GECCO 2010»
15 years 6 months ago
Implicit fitness and heterogeneous preferences in the genetic algorithm
This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested a...
Justin T. H. Smith
BMCBI
2006
201views more  BMCBI 2006»
15 years 6 months ago
Gene selection algorithms for microarray data based on least squares support vector machine
Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary ...
E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao