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» Evolutionary algorithms and matroid optimization problems
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GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
14 years 1 months ago
Genetic algorithms using low-discrepancy sequences
The random number generator is one of the important components of evolutionary algorithms (EAs). Therefore, when we try to solve function optimization problems using EAs, we must ...
Shuhei Kimura, Koki Matsumura
GECCO
2005
Springer
132views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolutionary testing of state-based programs
The application of Evolutionary Algorithms to structural test data generation, known as Evolutionary Testing, has to date largely focused on programs with input-output behavior. H...
Phil McMinn, Mike Holcombe
GECCO
2007
Springer
144views Optimization» more  GECCO 2007»
13 years 11 months ago
Multiobjective network design for realistic traffic models
Network topology design problems find application in several real life scenarios. However, most designs in the past either optimize for a single criterion like delay or assume sim...
Nilanjan Banerjee, Rajeev Kumar
CEC
2007
IEEE
13 years 11 months ago
Virtual reality high dimensional objective spaces for multi-objective optimization: An improved representation
This paper presents an approach for constructing improved visual representations of high dimensional objective spaces using virtual reality. These spaces arise from the solution of...
Julio J. Valdés, Alan J. Barton, Robert Orc...
CEC
2011
IEEE
12 years 7 months ago
Effective ranking + speciation = Many-objective optimization
—Multiobjective optimization problems have been widely addressed using evolutionary computation techniques. However, when dealing with more than three conflicting objectives (th...
Mario Garza-Fabre, Gregorio Toscano Pulido, Carlos...