Abstract—P systems are computational models that simulate the structure and functions of a living cell. Fuzzy logic deals with approximate reasoning rather than fixed. A classifier system is a machine learning system that helps to create new rules that can be used in classification in order to add new information to a given database. Cell aging is one of the main phases of any cell cycle. The rate of aging progression may vary from a person to another; furthermore, the cells of the same organ do not age in the same rate. In this paper, we are creating a classifier system with the structure and functions of P systems. The proposed classifier P system deals with imprecise biological data of cell aging, so the new rules generated by the system should be evaluated using a fuzzy rule base. Transition rules and inhibitors accompany the P system and executed in a parallel manner. I. INTRODUCTION A P system is a computational model that is based on the idea of cellular membrane structure and functions. It was presented by Gh. Paun in [1]. It is a branch of natural computing whose initial goal is to abstract computing models from the structure and the functioning of living cells [2]. The chemical reactions controlling the change of molecules are represented by evolution rules -also called multiset rewriting rules [1,3]- and the chemical reactions controlling transportation of molecules without changing them are represented by communication rules [1,3]. Communication rules can either be symport/antiport rules or rules with carriers [4]. P systems employ these rules in order to transform from a computational status to another. A simple transition P system is constructed of the form [3]: II = (O, C, µ, w 1 , w 2 ,…, w n, R 1 , R 2 ,…, R n , i o ) where: - O: The alphabet of objects, i.e. cellular molecules. - C: The alphabet of catalysts, if any. - µ: The membrane structure. It consists of n membranes labeled with 1,2,3,..,n. - w 1 , w 2 ,…, w n : The strings over O ∪ C, representing the multisets of objects initially present in all regions of the system membrane structure [3]. - R 1 , R 2 ,…, R n : The set of evolution rules associated with the regions of the system. An evolution rule can be a rule with an inhibitor. An object x is an inhibitor for a rule u→v, denoted by u→v| ¬x , if the rule is active only if inhibitor x is not present in the region [5]. - i o : The output region. It will take one of the labels 1,2,…n. Objects are assigned to rules by choosing rules and objects non-deterministically. Also, the chosen multiset of rules should be applicable to the chosen multiset of objects currently available. When no other rules can be applied on the current multiset of objects, the multiset of rules is said to be maximal. Different rules can be applied on different objects in parallel. We can conclude that P systems run in a maximally parallel non-determinitic manner [4]. A. Fuzzy Logic Fuzzy Logic was initiated in 1965 [6]. It is a multivalued logic that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc. Notions like rather tall or very fast can be formulated mathematically and processed by computers, in order to apply a more human-like way of thinking in the programming of computers [7]. Fuzzy logic is a useful theory to present inexact or imprecise terms like temperature (cold, worm or hot), height (very tall, tall, average, short, or very short). Each fuzzy term –linguistic term- is represented by a fuzzy set starting from the minimum value to the maximum value of each attribute. The membership function is a graphical representation of the magnitude of participation of each input. An example of the membership function of a linguistic variable is shown in fig. 1 [8]. Notice that trapezoidal functions or triangular functions or both can be used. B. Classifier Systems and genetic algorithms A Classifier System (CS) is a machine learning system that learns syntactically simple string rules, called classifiers, as introduced in [9]. A classifier system is a general machine learning system applicable to diverse environments, able to learn with incomplete information and classify the environment into hierarchies. It receives information about A Fuzzy Genetic Based Classifier P System to Predict Cell Aging Lamiaa Hassaan Ahmed, Amr Ahmed Badr, and Ibrahim Farag Abd El-Rahman Fig. 1. Examples of membership functions of a linguistic variable Manuscript received September 22, 2011; revised October 31, 2011. The authors are with Faculty of Computers and Information, Cairo University, Egypt (e-mail: lamia_work@yahoo.com; e-mail: a.badr.fci@gmail.com; e-mail: i.farag@fci-cu.edu.eg). Index Terms—Classifier systems, fuzzy p systems, natural computing, p systems, p system with inhibitors. International Journal of Computer and Electrical Engineering, Vol. 3, No. 6, December 2011 857