Gene Expression Programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally), and because of this apparently trivial fact, this new evolutionary system can finally make a difference, successfully assisting researchers in the design of robust and accurate computer models. As in nature, the linear chromosomes of GEP consist of the genetic material that is passed on with modification to the next generation. This, in other words, means that in GEP all the genetic modifications take place in the linear chromosomes (much easier to do than in complex branched structures as is done in GP), and it also means that only the linear chromosomes are transmitted in the process of reproduction (linear strings are much easier to replicate than complicated tree structures). And also as in nature, it's only during development that the information encoded in the chromosomes is finally expressed into fully developed computer programs or expression trees (ETs). Expression trees are sophisticated computer programs that are usually evolved to solve a particular problem and are therefore selected according to their fitness at solving that task. With time, populations of such computer programs (encoded, of course, in linear chromosomes) will discover new traits (thanks to genetic modification) and therefore will become better adapted to the particular environment chosen for their breeding (this environment defines obviously the problem at hand). And this means that, given enough time and that we've set the stage correctly, a good solution to the problem at hand will be discovered. So, GEP is a full-fledged genotype/phenotype system, with the genotype totally separated from the phenotype. In Genetic Programming, though, we have a totally different scenario: genotype and phenotype are one entangled mess or what is more formally called a simple replicator system. And the consequences of this are huge: the full-fledged genotype/phenotype system of GEP surpasses the old GP system by a factor of 100-60,000!
