Gene Expression Programming: a New Adaptive Algorithm for Solving Problems

Gene expression programming (GEP) is, like genetic algorithms (GAs) and genetic programming (GP), a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators [1]. The fundamental difference between the three algorithms resides in the nature of the individuals: in GAs the individuals are linear 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 encoded as linear strings of fixed length (the genome or chromosomes) which are afterwards expressed as nonlinear entities of different sizes and shapes (i.e., simple diagram representations or expression trees). If we have in mind the history of life on Earth (e.g., [2]), we can see that the difference between GAs and GP is only superficial: both systems use only one kind of entity which functions both as genome and body (phenome). These kinds of systems are condemned to have one of two limitations: if they are easy to manipulate genetically, they lose in functional complexity (the case of GAs); if they exhibit a certain Gene Expression Programming: A New Adaptive