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GECCO
2003
Springer

The Effect of Binary Matching Rules in Negative Selection

9 years 9 months ago
The Effect of Binary Matching Rules in Negative Selection
Negative selection algorithm is one of the most widely used techniques in the field of artificial immune systems. It is primarily used to detect changes in data/behavior patterns by generating detectors in the complementary space (from given normal samples). The negative selection algorithm generally uses binary matching rules to generate detectors. The purpose of the paper is to show that the low-level representation of binary matching rules is unable to capture the structure of some problem spaces. The paper compares some of the binary matching rules reported in the literature and study how they behave in a simple two-dimensional real-valued space. In particular, we study the detection accuracy and the areas covered by sets of detectors generated using the negative selection algorithm.
Fabio A. González, Dipankar Dasgupta, Jonat
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where GECCO
Authors Fabio A. González, Dipankar Dasgupta, Jonatan Gómez
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