Incollection,
Finding Trigonometric Identities with Tree Adjunct Grammar Guided Genetic Programming
Innovations in Intelligent Systems and Applications, volume 140 of Springer Studies in Fuzziness and Soft Computing, Springer-Verlag, Berlin, Germany, (January 2004)
Abstract
Introduction. Genetic programming (GP) may be seen as
a machine learning method, which induces a population
of computer programs by evolutionary means (Banzhaf et
al. 1998). Genetic programming has been used
successfully in generating computer programs for
solving a number of problems in a wide range of areas.
In (Hoai and McKay 2001), we proposed a framework for a
grammar-guided genetic programming system called
Tree-Adjunct Grammar Guided Genetic Programming
(TAG3P), which uses tree-adjunct grammars along with a
context-free grammar to set language bias in genetic
programming. The use of tree-adjunct grammars can be
seen as a process of building context-free grammar
guided programs in the two dimensional space. In this
chapter, we show some results of TAG3P on the
trigonometric identity discovery problem. The
organisation of the remainder of the chapter is as
follows. In section 2, we give a brief overview of
genetic programming, grammar guided genetic
programming, tree-adjunct grammars and TAG3P. The
problem of finding trigonometric identities will be
given in section 3. Section 4 contains the experiment
and results of TAG3P on that problem. The nature of
search space is empirically analysed and the bias by
selective adjunction is introduced. The last section
contains conclusion and future work.
