Emad Nabil

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 genetic algorithms that solved the same problem. The Membrane-Immune algorithm is also applied to generate a fuzzy rule based system to be used in breast cancer diagnosis. Generating a fuzzy rule system composes an exponential search space, which leads to the area of NP-complete problems. The algorithm is compared with five techniques and surpassed them. Last chapter presents a proposal of P Systems implementation using Cloud Computing. The proposed Implementation is illustrated by solving SAT problem.

A P system is a computability model which is biochemically inspired, it is a general distributed model, highly parallel, nondeterministic, based on the notion of a membrane structure. Till this moment, there is no exact idea about the real implementation of P systems. P systems are used in solving NP-complete problems in polynomial time, but with building the whole exponential search space. Cloud computing assume infinite memory and infinite processing power. This paper proposes an algorithm that uses the cloud resources in a fully parallel manner as a step towards P systems implementation, the nondeterminism property of P systems is certainly not maintained. The paper used the SAT problem as the case study.

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Abstract. The automatic diagnosis of breast cancer is an important medical problem. This paper hybridizes metaphors from cells membranes and intercommunication between compartments with clonal selection principle together with fuzzy logic to produce a fuzzy rule system in order to be used in diagnosis. The fuzzy-membrane-immune algorithm suggested were implemented and tested on the Wisconsin breast cancer diagnosis (WBCD) problem. The developed solution scheme is compared with five previous works based on neural networks and genetic algorithms. The algorithm surpasses all of them. There are two motivations for using fuzzy rules with the membrane-immune algorithm in the underline problem. The first is attaining high classification performance. The second is the possibility of attributing a confidence measure (degree of benignity or malignancy) to the output diagnosis, beside the simplicity of the diagnosis system, which means that the system is human interpretable.

Abstract. In this paper a membrane-immune algorithm is proposed, which is inspired from the structure of living cells and the vertebrate im-
mune system. The algorithm is used to solve one of the most famous combinatorial NP-complete problems, namely the Multiple Zero/One Knapsack Problem. Various heuristics, like genetic algorithms, have been devised to solve this class of combinatorial problems. The proposed algorithm is compared with two genetic based algorithms and overcame both of them. The algorithm is evaluated on nine benchmarks test problems and surpassed both of the genetic based algorithms in six problems, equaled with one of them in two problems and lost in one problem, which indicates that our algorithm surpasses in general genetic algorithms. We claim that the proposed algorithm is very useful in solving similar combinatorial NP-complete problems.

Abstract. Membrane Computing is an emergent and promising branch of Natural Computing. Designing P systems is heavy constitutes a difficult
problem. The candidate has often had an idea about the problem soluti onform. On the other hand, finding the exact and precise configurations
and rules is a hard task, especially if there is no tool used to help in the designing process. The clonal selection algorithm, which is inspired from
the vertebrate immune system, is introduced here to help in designing a P system that performs a specific task. This paper illustrates the use of
the clonal selection algorithm with adaptive mutation in P systems design and compares it with genetic algorithms previously used to achieve the
same purpose. Experimental results show that clonal selection algorithm surpasses genetic algorithms with a great difference.

The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic
Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. 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 re-
search, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algo-
rithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which
is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so
that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the well-
known Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance,
with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable.
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 our algorithm was verified through its application to a binary character recognition problem. The hybrid
algorithm overcomes also GAs and AIS and reached the convergence point before them.

Keywords: genetic algorithms, artificial immune system, fuzzy logic, breast cancer diagnosis, memory acquisition.

The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we
give an introduction to fuzzy systems, genetic algorithms and artificial immune system, and then we introduce a
hybrid algorithm that gathers the genetic algorithms with the artificial immune system in one algorithm. The genetic
algorithm, the artificial immune system and the hybrid algorithm were implemented and tested on the Wisconsin
breast cancer diagnosis (WBCD) problem in order to generate a fuzzy rule system for breast cancer diagnosis.
The hybrid algorithm generated a fuzzy system which reached the maximum classification ratio earlier than the
two other ones. The motivations of using fuzzy rules incorporate with evolutionary algorithms in the underline
problem are attaining high classification performance with the possibility of attributing a confidence measure
(degree of benignity or malignancy) to the output diagnosis beside the simplicity of the diagnosis system
which means that the system is human interpretable.