Grammar design for derivation tree based genetic programming systems

Abstract

Grammar-based genetic programming systems have gained interest in recent decades and are widely used nowadays. Although researchers normally present the grammar used to solve a certain problem, they seldom write about processes used to construct the grammar. This paper sheds some light on how to design a grammar that not only covers the search space, but also supports the search process in finding good solutions. The focus lies on context free grammar guided systems using derivation tree crossover and mutation, in contrast to linearised grammar based systems. Several grammars are presented encompassing the search space of sorting networks and show concepts which apply to general grammar design. An analysis of the search operators on different grammar is undertaken and performance examined on the sorting network problem. The results show that the overall structure for derivation trees created by the grammar has little effect on the performance, but still affects the genetic material changed by search operators.