Hybrid Artificial Immune System - Nabil, Emad;Badr, Amr;Abdelazim, Mostafa
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The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem-solving techniques. In this book, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. The hybrid algorithm used for evolving a fuzzy rule system to solve the well known Wisconsin Breast Cancer Diagnosis problem (WBCD). The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two…mehr

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Produktbeschreibung
The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem-solving techniques. In this book, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. The hybrid algorithm used for evolving a fuzzy rule system to solve the well known Wisconsin Breast Cancer Diagnosis problem (WBCD). The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of the hybrid algorithm was verified through its application to a binary character recognition problem, the hybrid algorithm overcomes also both GAs and AIS and reached the convergence point before them.
Autorenporträt
Dr. Emad Nabil works in the department of Computer Science, Faculty of Computers and Information, Cairo University. His research interests are Bio-Inspired Algorithms, P systems, Optimization and Bioinformatics. He prepared this work in collaboration with Prof. Amr Badr, Faculty of Computers and Information, Cairo University.