Text Classifiers Evolved on a Simulated DNA Computer

The use of synthetic DNA molecules for computing provides various insights to evolutionary computation. A molecular computing algorithm to evolve DNA-encoded genetic patterns has been previously reported. Here we improve on the previous work by studying the convergence behavior of the molecular evolutionary algorithm in the context of text classification problems. In particular, we study the error reduction behavior of the evolutionary learning algorithm, both theoretically and experimentally. The individuals represent decision lists of variable length and the whole population takes part in making probabilistic decisions. The evolutionary process is to change each individual towards correct classification of training data, which is based on an error minimization strategy. The evolved molecular classifiers show a performance competitive to the standard algorithms such as naive Bayes and neural network classifiers on the data set we studied. The possibility of molecular implementation by use of DNA-encoded individuals combined with simple molecular operations on a very big population distinguishes.