Evolutionary neural networks for time series prediction based on L-system and DNA coding method

The authors propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is an algorithm that is based on the characteristics of biological DNA and the growth of plants. The authors propose a construction method to make a DNA coding method for production rule of L-system. The L-system is based on the so-called parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based search methods, the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series and Sun spot data.