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GECCO
2000
Springer
114views Optimization» more  GECCO 2000»
13 years 8 months ago
Intelligent Recombination Using Individual Learning in a Collective Learning Genetic Algorithm
This paper introduces a new collective learning genetic algorithm (CLGA) which employs individual learning to do intelligent recombination based on a cooperative exchange of knowl...
Terry P. Riopka, Peter Bock
GPEM
2002
95views more  GPEM 2002»
13 years 4 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
BIOCOMP
2006
13 years 6 months ago
Using Neural Nets to Estimate Evolutionary Parameters
- The rapid growth in the amount of molecular genetic data being collected will, in many cases, require the development of new analytic methods for the analysis of that data. In th...
Chi-Chiang Lee, Paul Marjoram
EMO
2009
Springer
159views Optimization» more  EMO 2009»
13 years 11 months ago
Recombination for Learning Strategy Parameters in the MO-CMA-ES
The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population...
Thomas Voß, Nikolaus Hansen, Christian Igel
GECCO
2010
Springer
154views Optimization» more  GECCO 2010»
13 years 9 months ago
Evolutionary learning in networked multi-agent organizations
This study proposes a simple computational model of evolutionary learning in organizations informed by genetic algorithms. Agents who interact only with neighboring partners seek ...
Jae-Woo Kim