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119
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GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
15 years 9 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
2004
Springer
131views Optimization» more  GECCO 2004»
15 years 9 months ago
PolyEDA: Combining Estimation of Distribution Algorithms and Linear Inequality Constraints
Estimation of distribution algorithms (EDAs) are population-based heuristic search methods that use probabilistic models of good solutions to guide their search. When applied to co...
Jörn Grahl, Franz Rothlauf
103
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GECCO
2007
Springer
124views Optimization» more  GECCO 2007»
15 years 7 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 7 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...
136
Voted
CSC
2006
15 years 5 months ago
A New Migration Model For Distributed Genetic Algorithms
Genetic algorithms are heuristic search algorithm used in science, engineering and many other areas. They are powerful but slow because of their evolutionary nature that mimics th...
Taisir Eldos