Chemical Genetic Programming -Coevolution Between Genotypic Strings and Phenotypic Trees

Abstract. Chemical Genetic Programming (CGP) is a new method of genetic programming that introduced collision-based biochemical processes and realized dynamic mapping from genotypic strings to phenotypic trees. Chemical Genetic Programming (CGP) An initial set of rewriting rules is supplied that provides the system with the syntactical structure within which all programs must be generated. These rules allow only executable programs to be generated, but do not place any artificial limitations on the complexity or structure of these programs. Individuals are initially randomly generated, then evolved to form the population of successive generations. The fitness value of an individual is calculated using a correlation function that compares the target function with the individual's generated function. Based on their fitness values, cells, including both the DNA and the rewriting rules, are selected and then evolved. Evolution consists of mutation of DNA units, single point DNA crossover and molecular exchange between two cells, and selection of cells. Evolution adaptively changes not only the DNA information, but also the rewriting rules in cells. This enables the evolutionary optimization of the genotype (DNA) to phenotype (tree) mapping, by which we expect CGP to find the best translation grammar for creating a solution from the one-dimensional genotypic information. To examine the effectiveness of CGP, it was applied to a selected symbolic regression problem, and the results were compared against those of Grammatical Evolution (GE