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» Fitness Landscapes and Evolvability
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GECCO
2006
Springer
157views Optimization» more  GECCO 2006»
15 years 3 months ago
gLINC: identifying composability using group perturbation
We present two novel perturbation-based linkage learning algorithms that extend LINC [5]; a version of LINC optimised for decomposition tasks (oLINC) and a hierarchical version of...
David Jonathan Coffin, Christopher D. Clack
TCS
2008
14 years 11 months ago
Comparing evolutionary algorithms to the (1+1)-EA
In this paper, we study the conditions in which the (1+1)-EA compares favorably to other evolutionary algorithms (EAs) in terms of fitness function distribution at given iteration...
Pavel A. Borisovsky, Anton V. Eremeev
HCI
2009
14 years 9 months ago
Distributed Intelligence and Scaffolding in Support of Cognitive Health
Computers have dramatically changed the social landscape and living practices in the 21st century. Most of those changes have empowered typically abled adults, while it is only in ...
Stefan Carmien, Randal A. Koene
CEC
2008
IEEE
15 years 1 months ago
Efficient evolution of ART neural networks
Abstract-- Genetic algorithms have been used to evolve several neural network architectures. In a previous effort, we introduced the evolution of three well known ART architects; F...
Assem Kaylani, Michael Georgiopoulos, Mansooreh Mo...
GECCO
2006
Springer
166views Optimization» more  GECCO 2006»
15 years 3 months ago
Comparing genetic robustness in generational vs. steady state evolutionary algorithms
Previous research has shown that evolutionary systems not only try to develop solutions that satisfy a fitness requirement, but indirectly attempt to develop genetically robust so...
Josh Jones, Terry Soule