Application of advanced Grammatical Evolution to function prediction problem

Grammatical Evolution (GE) is one of the evolutionary algorithms to find functions and programs, which can deal according to a syntax with tree structure by one-dimensional chromosome of Genetic Algorithm. An original GE starts from the definition of the syntax by means of Backus Naur Form (BNF). Chromosome in binary number is translated to that in decimal number. The BNF syntax selects according to the remainder of the decimal number with respect to the total number of candidate rules. In this study, we will introduce three schemes for improving the convergence property of the original GE. In numerical examples, the original GE is compared in function identification problem with the Genetic Programming (GP), which is one of the most popular evolutionary algorithm to find unknown functions or programs. Three algorithms are compared in Santa Fe trail problem and prediction problem of Nikkei stock average, which finds programs to control artificial ants collecting foods. The results show that the efficiency of schemes depends on the problem to be solved and that the schemes 1 and 2 are effective for Santa Fe trail problem and prediction problem of Nikkei stock average, respectively. (C) 2010 Elsevier Ltd. All rights reserved.