Introduction: The Biological Perspective

Abstract

The aim of this chapter is to bring into focus the basic differences between gene expression programming (GEP) and its predecessors, genetic algorithms (GAs) and genetic programming (GP). All three algorithms belong to the wider class of Genetic Algorithms (the use of capitals here is meant to distinguish this wider class from the canonical GA) as all of them use populations of individuals, select the individuals according to fitness, and introduce genetic variation using one or more genetic operators. The fundamental difference between the three algorithms resides in the nature of the individuals: in GAs the individuals are symbolic strings of fixed length (chromosomes); in GP the individuals are nonlinear entities of different sizes and shapes (parse trees); and in GEP the individuals are also nonlinear entities of different sizes and shapes (expression trees), but these complex entities are encoded as simple strings of fixed length (chromosomes).