A Framework for Evolving Multi-Shaped Detectors in Negative Selection
A Framework for Evolving Multi-Shaped Detectors in Negative Selection
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DOI：
10.1109/foci.2007.371503
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发表时间：
2007-04
期刊：
影响因子：
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通讯作者：
S. Balachandran;D. Dasgupta;Fernando Niño;D. Garrett
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文献类型：
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作者：
S. Balachandran;D. Dasgupta;Fernando Niño;D. Garrett
本文提出了一个框架，以产生多形检测器与价值否定选择算法（NSA）。特别地，检测器可以在非自我空间中采取超矩形、超球体和超椭圆的形式。这些新的模式检测器（在互补空间）进化使用遗传搜索（结构化遗传算法），它使用分层基因组结构和基因激活机制来编码多个检测器的形状。这种遗传搜索（结构化GA）允许保持不同的形状，同时有助于在表达的表型中增殖最适合的检测器形状。结果表明，与其他NSA方法（仅使用单一形状的检测器）相比，使用更少的检测器可以实现对非自我空间的显著覆盖。在这项工作中使用的统一表示方案和进化机制可以作为一个基线，进一步扩展使用几个形状，提供了一个有效的覆盖非自我空间。
This paper presents a framework to generate multi-shaped detectors with valued negative selection algorithms (NSA). In particular, detectors can take the form of hyper-rectangles, hyper-spheres and hyper-ellipses in the non-self space. These novel pattern detectors (in the complement space) are evolved using a genetic search (the structured genetic algorithm), which uses hierarchical genomic structures and a gene activation mechanism to encode multiple detector shapes. This genetic search (the structured GA) allows in maintaining diverse shapes while contributing to the proliferation of best suited detector shapes in expressed phenotype. The results showed that a significant coverage of the non-self space could be achieved with fewer detectors compared to other NSA approaches (using only single-shaped detectors). The uniform representation scheme and the evolutionary mechanism used in this work can serve as a baseline for further extension to use several shapes, providing an efficient coverage of non-self space.