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GECCO
2005
Springer
141views Optimization» more  GECCO 2005»
14 years 1 months ago
Behavior of finite population variable length genetic algorithms under random selection
In this work we provide empirical evidence that shows how a variable-length genetic algorithm (GA) can naturally evolve shorter average size populations. This reduction in chromos...
Hal Stringer, Annie S. Wu
CEC
2009
IEEE
14 years 2 months ago
Evolving modular neural-networks through exaptation
— Despite their success as optimization methods, evolutionary algorithms face many difficulties to design artifacts with complex structures. According to paleontologists, living...
Jean-Baptiste Mouret, Stéphane Doncieux
CEC
2008
IEEE
14 years 2 months ago
Creating edge detectors by evolutionary reinforcement learning
— In this article we present results from experiments where a edge detector was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is c...
Nils T. Siebel, Sven Grünewald, Gerald Sommer
JNCA
2011
123views more  JNCA 2011»
13 years 2 months ago
Empirical tests of anonymous voice over IP
Voice over IP (VoIP) is an important service on the Internet, and privacy for VoIP calls will be increasingly important for many people. Providing this privacy, however, is challen...
Marc Liberatore, Bikas Gurung, Brian Neil Levine, ...
GECCO
2007
Springer
182views Optimization» more  GECCO 2007»
14 years 1 months ago
An analysis of the effects of population structure on scalable multiobjective optimization problems
Multiobjective evolutionary algorithms (MOEA) are an effective tool for solving search and optimization problems containing several incommensurable and possibly conflicting objec...
Michael Kirley, Robert L. Stewart