A Randomized Real-Valued Negative Selection Algorithm
A Randomized Real-Valued Negative Selection Algorithm
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DOI：
10.1007/978-3-540-45192-1_25
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发表时间：
2003-09
期刊：
影响因子：
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通讯作者：
F. González;D. Dasgupta;Fernando Niño
中科院分区：
文献类型：
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作者：
F. González;D. Dasgupta;Fernando Niño
本文提出了一种具有良好数学基础的实值否定选择算法，它克服了以前方法的一些缺点[11]。具体地说，它可以很好地估计覆盖非我空间所需的最优检测器数量，并通过具有证明的收敛性质的优化算法来最大化非我覆盖。该方法是一种基于蒙特卡罗方法的随机化算法。通过实验验证了算法设计时的假设条件，并对算法的性能进行了评估。
This paper presents a real-valued negative selection algorithm with good mathematical foundation that solves some of the drawbacks of our previous approach [11]. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. The proposed method is a randomized algorithm based on Monte Carlo methods. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.