Design Methods For Cell-Like Membrane Systems

Membrane computing (MC), is an important research field of computer science, aiming to abstract computing ideas and models from the structure and the functioning of living cells, as well as from the way the cells are organized in tissues or higher order structures. The models investigated in membrane computing area are called membrane systems or P systems, which are distributed and parallel computing model. Theoretically, many classes of P systems are able of simulating Turing machines, hence they are computationally complete.Membrane computing is an active research field currently. Theoretical research on membrane computing has been curried out by using mathematical method and formal languages. To date, many different P systems have been defined and used for theoretical research as well as a vehicle to represent various problems from different fields. Designing a P system in order to perform a specific task is a heavy hand-made work, especially if there is no tool used to help in the designing process. All the existing P systems are meticulous designed by researchers. Moreover, little work on the design method of P systems has been reported in the literature. This paper makes research on the automatically evolving a P system by employing a quantum-inspired evolutionary algorithm (QIEA) and the P-Lingua simulator.The main research work and research fruits are as follows:1、The principle of membrane computing is introduced. Then, Availabe softwares for simulating cell-like P systems are summarized. Especially the P-Lingua simulator is introduced in detail. An example is illustrated to show the application of the P-Lingua to simulate a cell-like P system.2、By describing and analyzing the problem about designing a P system, an optimum design method for simple cell-like P systems is presented. To be specific, the method is introduced from the following aspects:the design thought way, the QIEAs, the coding technique of P systems and the evaluation of them. Experiments are carried out on designing a simple P system for computing a mathematical operation, namely, square of four, the results show that QIEA surpasses genetic algorithms. In addition, the design approach is extended to deal with designing another P system for computing square of any natural number, and the results also show that the presented design approach is effective.3、In order to improve the optimum design method, new fitness functions are proposed and local disturbance strategy is introduced based on the original QIEA. On base of the improved approach, experiments are curried out on designing three different P systems which are for computing all the square numbers, computing an algebraic expression and generating formal language respectively. Experiment results show that in the same conditions different P systems can be obtained by using different fitness function. With the addition of local disturbance strategy in QIEA, it achieves better results compared with the original QIEA and random searching method.4、Based on the trace of a P system, a new optimum design method for cell-like P systems is presented. The method is applied to design a P system for computing all the square numbers. The results illustrate that optimal P system using QIEA from the trace of a P system is feasible and effective.This work is supported by the National Natural Science Foundation of China (61170016), the program for New Century Excellent Talents in university (NCET-11-0715) and the project sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.