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» Multiobjective Evolutionary Algorithms: Analyzing the State-...
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GECCO
2009
Springer
125views Optimization» more  GECCO 2009»
13 years 10 months ago
Preserving population diversity for the multi-objective vehicle routing problem with time windows
The Vehicle Routing Problem’s main objective is to find the lowest-cost set of routes to deliver goods to customers, which have a service time window, using a fleet of identic...
Abel Garcia-Najera
GECCO
2007
Springer
308views Optimization» more  GECCO 2007»
13 years 12 months 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...
CEC
2005
IEEE
13 years 11 months ago
Theoretical comparisons of search dynamics of genetic algorithms and evolution strategies
Genetic algorithms (GAs) and evolution strategies (ESs) are two widely used evolutionary algorithms. The main differences between GAs and ESs lie in their representations and varia...
Tatsuya Okabe, Yaochu Jin, Bernhard Sendhoff
CEC
2009
IEEE
14 years 16 days ago
Evolving modular neural-networks through exaptation
— Despite their success as optimization methods, evolutionary algorithms face many difficulties to design artifacts with complex structures. According to paleontologists, living...
Jean-Baptiste Mouret, Stéphane Doncieux
GECCO
2009
Springer
142views Optimization» more  GECCO 2009»
14 years 10 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...