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» On the Brittleness of Evolutionary Algorithms
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GPEM
2007
119views more  GPEM 2007»
15 years 6 months ago
Genomic mining for complex disease traits with "random chemistry"
Our rapidly growing knowledge regarding genetic variation in the human genome offers great potential for understanding the genetic etiology of disease. This, in turn, could revolut...
Margaret J. Eppstein, Joshua L. Payne, Bill C. Whi...
GPEM
2002
95views more  GPEM 2002»
15 years 6 months ago
On Appropriate Adaptation Levels for the Learning of Gene Linkage
A number of algorithms have been proposed aimed at tackling the problem of learning "Gene Linkage" within the context of genetic optimisation, that is to say, the problem...
James Smith
GECCO
2009
Springer
142views Optimization» more  GECCO 2009»
16 years 28 days ago
A stopping criterion based on Kalman estimation techniques with several progress indicators
The need for a stopping criterion in MOEA’s is a repeatedly mentioned matter in the domain of MOOP’s, even though it is usually left aside as secondary, while stopping criteri...
José Luis Guerrero, Jesús Garc&iacut...
GECCO
2007
Springer
308views Optimization» more  GECCO 2007»
16 years 16 days ago
Multiobjective clustering with automatic k-determination for large-scale data
Web mining - data mining for web data - is a key factor of web technologies. Especially, web behavior mining has attracted a great deal of attention recently. Behavior mining invo...
Nobukazu Matake, Tomoyuki Hiroyasu, Mitsunori Miki...
GECCO
2007
Springer
184views Optimization» more  GECCO 2007»
16 years 16 days ago
Experimental analysis of binary differential evolution in dynamic environments
Many real-world optimization problems are dynamic in nature. The interest in the Evolutionary Algorithms (EAs) community in applying EA variants to dynamic optimization problems h...
Alp Emre Kanlikilicer, Ali Keles, Sima Uyar