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GECCO
2007
Springer
157views Optimization» more  GECCO 2007»
15 years 3 months ago
A doubly distributed genetic algorithm for network coding
We present a genetic algorithm which is distributed in two novel ways: along genotype and temporal axes. Our algorithm first distributes, for every member of the population, a su...
Minkyu Kim, Varun Aggarwal, Una-May O'Reilly, Muri...
GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
15 years 3 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
EOR
2006
125views more  EOR 2006»
14 years 9 months ago
A genetic algorithm for robotic assembly line balancing
Flexibility and automation in assembly lines can be achieved by the use of robots. The robotic assembly line balancing (RALB) problem is defined for robotic assembly line, where d...
Gregory Levitin, Jacob Rubinovitz, Boris Shnits
GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
15 years 3 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 1 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