Membrane Computing (P Systems) is an emergent and promising branch of Natural Computing. Designing P Systems is a heavy difficult problem. Until now there is no tool exists that can help in designing of P systems. This book shows how to use clonal selection algorithm with adaptive mutation in the design of P systems. In Addition the book proposes a Membrane-Immune algorithm that is inspired from the structure of living cells and the vertebrate immune system. The algorithm is tested by solving the Multiple Zero/One Knapsack Problem. The Membrane-Immune algorithm surpassed two variants of...

Vaccination is a method of stimulating resistant in the human body to specific diseases using microorganisms (bacteria or viruses) that have been modified or killed. Currently, the available vaccines are composed of killed or life attenuated the whole pathogens before being injected to the human body. Despite the advantages of such vaccines, there are a lot of potential safety problems such as incomplete inactivation of the killed pathogens or the evolution of the attenuated virus. Instead of the entire microbe, subunit vaccines include only the antigens that best stimulate the immune system....

Identification of Major Histocompatibility Complex (MHC) binding peptides is an important step in the selection of T-Cell epitope candidates that are suitable for usage in new vaccines. The binding groove of the MHC Class-II molecule is opened at both sides, which allows for high variability in length of the peptides that bind to this molecule and consequently; complicates the prediction of the binding core motif. An accurate and efficient computational approach for the prediction of such peptides can greatly reduce the time and cost required for the design of new vaccines. EpiGASVM, a new...

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...