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» Genetic programming and evolutionary generalization
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GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
15 years 5 months ago
GAMM: genetic algorithms with meta-models for vision
Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an ex...
Greg Lee, Vadim Bulitko
GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
15 years 3 months ago
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
The Negative Slope Coefficient (nsc) is an empirical measure of problem hardness based on the analysis of offspring-fitness vs. parent-fitness scatterplots. The nsc has been teste...
Riccardo Poli, Leonardo Vanneschi
GECCO
2006
Springer
190views Optimization» more  GECCO 2006»
15 years 3 months ago
Design synthesis of microelectromechanical systems using genetic algorithms with component-based genotype representation
An automated design synthesis system based on a multi-objective genetic algorithm (MOGA) has been developed for the optimization of surface-micromachined MEMS devices. A hierarchi...
Ying Zhang, Raffi R. Kamalian, Alice M. Agogino, C...
GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
15 years 5 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
153views Optimization» more  GECCO 2005»
15 years 5 months ago
Finding needles in haystacks is harder with neutrality
This research presents an analysis of the reported successes of the Cartesian Genetic Programming method on a simplified form of the Boolean parity problem. We show the method of...
M. Collins