Research And Implementation Of An Improved Gene Expression Programming (Gep) Algorithm For Synthesis Of Reversible Logic Circuits

Gene Expression Programming (GEP) algorithm is a new evolutionary computing model, proposed by Candida Ferreira in2001, which is a new self-adaptive evolutionary algorithms based on genotype and phenotype. GEP algorithm combines the advantages of genetic algorithm (GA) and genetic programming (GP), but also to overcome the respective shortcomings of both. In GEP algorithm the individuals are encoded as linear strings of fixed length (genotype) which are like the chromosomes of GA, and afterwards expressed as nonlinear entities of different sizes and shapes (phenotype) which are like expression trees of GP. So it can use a simple code to solve complex problems, and can easily carry out selection, crossover and mutation genetic operations. In solving many complex problems, GEP algorithm is much faster than genetic programming in2-4magnitudes.Reversible logic circuit is composed of reversible logic gates in cascade, it has completely reversible characteristics and can effectively solve the problem of the energy consumption of integrated circuit. Reversible logic synthesis is the use of the given reversible logic gates, according to the network without fan in and out, no feedback, to achieve the appropriate reversible logic circuits and makes the quantum cost as small as possible. However, reversible logic gate is based on XOR operation; it makes ESOP expression become the most suitable expression for reversible logic instead of SOP expression. Gene Expression Programming has the ability of automatic discovery of optimal expression on the lack of knowledge and experiences, so it is expected to solve the comprehensive and optimization problem of reversible logic circuit.This paper focuses on the research and Realization of a new evolutionary design method of reversible logic circuits-GEP algorithm. This paper first analyzes the characteristics and needs of reversible logic synthesis, introduces and compares the advantages and disadvantages of reversible logic synthesis methods. Secondly, study on the gene expression programming algorithm and put forward a suitable GEP algorithm according to the characteristics and demands of reversible logic synthesis. Finally, the GEP algorithm is improved, the optimization can be applied to multi output logic function. Preliminary experiments indicates that the GEP algorithm is a feasible and effective evolutionary design method of reversible logic circuits, which is able to automatically gain the most simplified ESOP expressions used for constructing reversible logic networks based on anticipated logic functions. Therefore, this paper has a certain reference value and guiding significance for the study of reversible logic synthesis and optimization algorithm.