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» Biological Inspiration for Artificial Immune Systems
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GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
15 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
IWINAC
2009
Springer
15 years 4 months ago
Brain Complexity: Analysis, Models and Limits of Understanding
Manifold initiatives try to utilize the operational principles of organisms and brains to develop alternative, biologically inspired computing paradigms. This paper reviews key fea...
Andreas Schierwagen
131
Voted
ECAL
1995
Springer
15 years 1 months ago
Contextual Genetic Algorithms: Evolving Developmental Rules
A genetic algorithm scheme with a stochastic genotype/phenotype relation is proposed. The mechanisms responsible for this intermediate level of uncertainty, are inspired by the bio...
Luis Mateus Rocha
ALIFE
2005
14 years 10 months ago
Flexible Couplings: Diffusing Neuromodulators and Adaptive Robotics
Recent years have seen the discovery of freely diffusing gaseous neurotransmitters, such as nitric oxide (NO), in biological nervous systems. A type of artificial neural network (A...
Andrew Philippides, Phil Husbands, Tom Smith, Mich...
ICARIS
2005
Springer
15 years 3 months ago
A Comparative Study of Real-Valued Negative Selection to Statistical Anomaly Detection Techniques
The (randomized) real-valued negative selection algorithm is an anomaly detection approach, inspired by the negative selection immune system principle. The algorithm was proposed t...
Thomas Stibor, Jonathan Timmis, Claudia Eckert