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» Metaphor for learning: an evolutionary algorithm
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GECCO
2009
Springer
199views Optimization» more  GECCO 2009»
14 years 10 days ago
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Encouraging exploration, typically by preserving the diversity within the population, is one of the most common method to improve the behavior of evolutionary algorithms with dece...
Jean-Baptiste Mouret, Stéphane Doncieux
GECCO
2007
Springer
195views Optimization» more  GECCO 2007»
14 years 1 months ago
MILCS: a mutual information learning classifier system
This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifical...
Robert Elliott Smith, Max Kun Jiang
GECCO
2005
Springer
175views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolution of multi-loop controllers for fixed morphology with a cyclic genetic algorithm
Cyclic genetic algorithms can be used to generate single loop control programs for robots. While successful in generating controllers for individual leg movement, gait generation,...
Gary B. Parker, Ramona Georgescu
CEC
2009
IEEE
14 years 2 months ago
Hyper-learning for population-based incremental learning in dynamic environments
— The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. Recently, the PBIL algorithm has been applied...
Shengxiang Yang, Hendrik Richter
GPEM
2002
95views more  GPEM 2002»
13 years 7 months ago
On Appropriate Adaptation Levels for the Learning of Gene Linkage
A number of algorithms have been proposed aimed at tackling the problem of learning "Gene Linkage" within the context of genetic optimisation, that is to say, the problem...
James Smith