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 C. FERREIRA Complex Systems, 13 (2): 87-129, 2001

Gene Expression Programming: A New Adaptive Algorithm for Solving Problems

The structural organization of GEP genes is better understood in terms of open reading frames (ORFs). In biology, an ORF, or coding sequence of a gene, begins with the “start” codon, continues with the amino acid codons, and ends at a termination codon. However, a gene is more than the respective ORF, with sequences upstream from the start codon and sequences downstream from the stop codon. Although in GEP the start site is always the first position of a gene, the termination point does not always coincide with the last position of a gene. It is common for GEP genes to have noncoding regions downstream from the termination point. (For now we will not consider these noncoding regions, because they do not interfere with the product of expression.)

Consider, for example, the algebraic expression:

 (3.1)

which can also be represented as a diagram or ET:

where “Q” represents the square root function. This kind of diagram representation is in fact the phenotype of GEP individuals, being the genotype easily inferred from the phenotype as follows:

 01234567 Q*+-abcd (3.2)

which is the straightforward reading of the ET from left to right and from top to bottom. Expression (3.2) is an ORF, starting at “Q” (position 0) and terminating at “d” (position 7). These ORFs were named K-expressions (from the Karva language, the name I chose for the language of GEP). Note that this ordering differs from both the postfix and prefix expressions used in different GP implementations with arrays or stacks [6].

The inverse process, that is, the translation of a K-expression into an ET, is also very simple. Consider the following K-expression:

 01234567890 Q*+*a*Qaaba (3.3)

The start position (position 0) in the ORF corresponds to the root of the ET. Then, below each function are attached as many branches as there are arguments to that function. The assemblage is complete when a baseline composed only of terminals (the variables or constants used in a problem) is formed. In this case, the following ET is formed:

Looking only at the structure of GEP ORFs, it is difficult or even impossible to see the advantages of such a representation, except perhaps for its simplicity and elegance. However, when ORFs are analyzed in the context of a gene, the advantages of such representation become obvious. As stated previously, GEP chromosomes have fixed length and are composed of one or more genes of equal length. Therefore the length of a gene is also fixed. Thus, in GEP, what varies is not the length of genes (which is constant), but the length of the ORFs. Indeed, the length of an ORF may be equal to or less than the length of the gene. In the first case, the termination point coincides with the end of the gene, and in the second case, the termination point is somewhere upstream from the end of the gene.

So, what is the function of these noncoding regions in GEP genes? They are, in fact, the essence of GEP and evolvability, for they allow modification of the genome using any genetic operator without restrictions, always producing syntactically correct programs without the need for a complicated editing process or highly constrained ways of implementing genetic operators. Indeed, this is the paramount difference between GEP and previous GP implementations, with or without linear genomes (for a review on GP with linear genomes see [7]).

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