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GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
14 years 3 months ago
Applying both positive and negative selection to supervised learning for anomaly detection
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...
Xiaoshu Hang, Honghua Dai
GECCO
2006
Springer
141views Optimization» more  GECCO 2006»
14 years 1 months ago
Coevolution of neural networks using a layered pareto archive
The Layered Pareto Coevolution Archive (LAPCA) was recently proposed as an effective Coevolutionary Memory (CM) which, under certain assumptions, approximates monotonic progress i...
German A. Monroy, Kenneth O. Stanley, Risto Miikku...
GECCO
2008
Springer
161views Optimization» more  GECCO 2008»
13 years 11 months ago
Neuro-evolution for a gathering and collective construction task
In this paper we apply three Neuro-Evolution (NE) methods as controller design approaches in a collective behavior task. These NE methods are Enforced Sub-Populations, MultiAgent ...
D. W. F. van Krevelen, Geoff S. Nitschke
CEC
2009
IEEE
14 years 4 months ago
An adaptive learning particle swarm optimizer for function optimization
— Traditional particle swarm optimization (PSO) suffers from the premature convergence problem, which usually results in PSO being trapped in local optima. This paper presents an...
Changhe Li, Shengxiang Yang
GECCO
2010
Springer
180views Optimization» more  GECCO 2010»
14 years 2 months ago
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
This paper presents results of the BBOB-2009 benchmarking of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. The runtime ...
Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen ...