A Framework For Tree-Adjunct Grammar Guided Genetic Programming

In this paper we propose the framework for a grammar-guided genetic programming system called Tree-Adjunct Grammar Guided Genetic Programming (TAGGGP). Some intuitively promising aspects of the model compared with other grammar-guided evolutionary methods are also highlighted. 1 Introduction Genetic programming (GP) is considered to be a machine learning method, which induces a population of computer programs by evolutionary means ([Banzhat et al, 1998]). Genetic programming has been used successfully in generating computer programs for solving a numbers of problems from various areas. In this paper, we propose a framework for a grammar-guided genetic programming system called Tree-Adjunct Grammar Guided Genetic Programming (TAGGGP), which uses tree-adjunct grammars to guide genetic programming. The use of tree-adjunct grammars can be seen as a process of building grammar guided programs in the two dimensional space. The organization of the remainder of the paper is as follows. In section 2, we will give a brief on genetic programming, grammar-guided genetic programming, and tree-adjoining grammars. The main theme of TAGGGP will be given in section 3. The paper concludes with section 4, which contains the discussion on some intuitively promising aspects of TAGGGP in compared with other grammar-guided genetic programming schemes and future work.