A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is
A practical study on shape space and its occupancy in negative selection
IEEE Congress on Evolutionary Computation
The success of a negative selection algorithm depends on its detectors. A shape space, conceptually, is where selves, detectors, and antigens reside. These detectors are expected to fully cover the whole shape space. The better the coverage; the better the detection rate. However, this assumption brings a major challenge to negative selection experiments -the scalability problem, where the experiments cannot process real life datasets in a timely manner. On the other hand, with any real lifedoi:10.1109/cec.2010.5586266 dblp:conf/cec/MaTS10 fatcat:sfagxybtefgjxkztwgd3rhn7ea