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GECCO
2008
Springer
186views Optimization» more  GECCO 2008»
13 years 8 months ago
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate...
A. K. M. Khaled Ahsan Talukder, Michael Kirley, Ra...
PR
2008
131views more  PR 2008»
13 years 7 months ago
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks. ...
Salvador García, José Ramón C...
GECCO
2005
Springer
140views Optimization» more  GECCO 2005»
14 years 1 months ago
A comparison of evolutionary algorithms for system-level diagnosis
The size and complexity of systems based on multiple processing units demand techniques for the automatic diagnosis of their state. System-level diagnosis consists in determining ...
Bogdan Tomoyuki Nassu, Elias Procópio Duart...
ENGL
2007
121views more  ENGL 2007»
13 years 7 months ago
A Comparison between Genetic Algorithms and Evolutionary Programming based on Cutting Stock Problem
—Genetic Algorithms (GA) and Evolutionary Programming (EP) are two well-known optimization methods that belong to the class of Evolutionary Algorithms (EA). Both methods have gen...
Raymond Chiong, Ooi Koon Beng
CEC
2008
IEEE
14 years 2 months ago
Auto-tuning fuzzy granulation for evolutionary optimization
—Much of the computational complexity in employing evolutionary algorithms as optimization tool is due to the fitness function evaluation that may either not exist or be computat...
Mohsen Davarynejad, Mohammad R. Akbarzadeh-Totonch...