By Saif ur Rehman, Muhammed Naeem Ahmed Khan (auth.), Álvaro Herrero, Emilio Corchado, Carlos Redondo, Ángel Alonso (eds.)
This quantity of Advances in clever and smooth Computing comprises accredited papers provided on the 3rd overseas convention on Computational Intelligence in defense for info platforms (CISIS'10), which was once held within the attractive and old urban of León, Spain, in November, 2010.
Since it beginning, the most objective of CISIS meetings has been to set up a extensive and interdisciplinary discussion board for Computational Intelligence and linked studying Paradigms, that are enjoying more and more very important roles in quite a few software parts concerning safeguard in details Systems.
CISIS'10 supplied a huge and interdisciplinary discussion board to offer the newest advancements in different very lively medical components akin to computing device studying, Infrastructure safety, clever tools in power and Transportation, community defense, Biometry, Cryptography, High-performance and Grid Computing, and business point of view between others. the worldwide function of the CISIS sequence of meetings has been to shape a extensive and interdisciplinary assembly flooring delivering the chance to engage with the prime study workforce and industries actively curious about the serious zone of safety, and feature an image of the present recommendations followed in sensible domains.
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Extra info for Computational Intelligence in Security for Information Systems 2010: Proceedings of the 3rd International Conference on Computational Intelligence in Security for Information Systems (CISIS’10)
Fig. 1 Genetic operators implemented for search queries The F-Score was used as a ﬁtness function. An experimental evaluation of such an information retrieval system showed that the GP can ﬁnd search expression describing fuzzy sets of relevant documents [9, 10]. 4 Genetic Evolution of Fuzzy Classifier The algorithm for evolutionary query optimization was applied to the evolution of a general symbolic fuzzy classiﬁer. In this work, we have evolved a fuzzy classiﬁer for quality prediction in an industrial manufacturing process.
148 1 . . 160 1 . . 160 0 .. . . . 025 0 (b) Product features data set as an IRS index matrix D. 160⎟ ⎟ ⎜ D=⎜ . .. ⎟ .. ⎝ .. . 025 We have implemented the GP for the evolution of fuzzy classiﬁers. The fuzzy classiﬁer that was evolved by the algorithm corresponds to a search expression that describes the class of defective products in terms of product features. The parameters of the executed GP are shown in Table 3. 399872. e. without ﬂawless products being marked as defective). 5 was evolved in 10 cases out of 12 independent runs.
Without ﬂawless products being marked as defective). 5 was evolved in 10 cases out of 12 independent runs. An example of several evolved fuzzy classiﬁers is shown in Table 4. The best classiﬁer found by the algorithm was Q1. It is indeed a perfect expression describing defective products in the learning data set. 3999 in terms of its F-Score, but also in terms of precision and recall because it describes defective products only. The symbolic nature of the GP output gives us valuable information about the features that indicate product defectiveness.