Solving Travelling Salesman Problem in a Simulation of Genetic Algorithms with DNA

In this paper it is explained how to solve a fully connected N-City travelling salesman problem (TSP) using a genetic algorithm. A crossover operator to use in the simulation of a genetic algorithm (GA) with DNA is presented. The aim of the paper is to follow the path of creating a new computational model based on DNA molecules and genetic operations. This paper solves the problem of exponentially size algorithms in DNA computing by using biological methods and techniques. After individual encoding and fitness evaluation, a protocol of the next step in a GA, crossover, is needed. This paper also shows how to make the GA faster via different populations of possible solutions.

XML Presentation of Documents Used for Data Exchange in Management of Genetic Resources

In the global strategy for preservation genetic resources of farm animals the implementation of information technology is of great importance. In this regards platform independent information tools and approaches for data exchange are needed in order to obtain aggregate values for regions and countries of spreading a separate breed. The current paper presents a XML based solution for data exchange in management genetic resources of farm animals’ small populations. There are specific requirements to the exchanged documents that come from the goal of data analysis. Three main types of documents are distinguished and their XML formats are discussed. DTD and XML Schema for each type are suggested. Some examples of XML documents are given also.

Self Evolving Character Recognition Using Genetic Operators

In this paper, a novel approach for character recognition has been presented with the help of genetic operators which have evolved from biological genetics and help us to achieve highly accurate results. A genetic algorithm approach has been described in which the biological haploid chromosomes have been implemented using a single row bit pattern of 315 values which have been operated upon by various genetic operators. A set of characters are taken as an initial population from which various new generations of characters are generated with the help of selection, crossover and mutation. Variations of population of characters are evolved from which the fittest solution is found by subjecting the various populations to a new fitness function developed. The methodology works and reduces the dissimilarity coefficient found by the fitness function between the character to be recognized and members of the populations and on reaching threshold limit of the error found from dissimilarity, it recognizes the character. As the new population is being generated from the older population, traits are passed on from one generation to another. We present a methodology with the help of which we are able to achieve highly efficient character recognition.