Editing Linear genetic programming (section)

==Examples of LGP programs==

Because LGP programs are basically represented by a linear sequence of instructions, they are simpler to read and to operate on than their tree-based counterparts. For example, a simple program written to solve a Boolean function problem with 3 inputs (in R1, R2, R3) and one output (in R0), could read like this:
<syntaxhighlight lang="bash">
R4 = R2 AND R3
R0 = R1 OR R4
R0 = R3 AND R0
R4 = R2 AND R4   # This is a non-effective instruction
R0 = R0 OR R2
</syntaxhighlight>
R1, R2, R3 have to be declared as input (read-only) registers, while R0 and R4 are declared as calculation (read-write) registers. This program is very simple, having just 5 instructions. But mutation and crossover operators could work to increase the length of the program, as well as the content of each of its instructions.

Note that one instruction is non-effective or an intron (marked), since it does not impact the output register R0. Recognition of those instructions is the basis for the intron removal algorithm which is used analyze code prior to execution. Technically, this happens by copying an individual and then run the intron removal once. The copy with removed introns is then executed as many times as dictated by the number of training cases. Notably, the original individual is left intact, so as to continue participating in the evolutionary process. It is only the copy that is executed that is compressed by removing these "structural" introns.

Another simple program, this one written in the LGP language [https://github.com/arturadib/slash-a Slash/A] looks like a series of instructions separated by a slash:
<syntaxhighlight lang="bash">
input/   # gets an input from user and saves it to register F
0/       # sets register I = 0
save/    # saves content of F into data vector D[I] (i.e. D[0] := F)
input/   # gets another input, saves to F
add/     # adds to F current data pointed to by I (i.e. F := F + D[0])
output/. # outputs result from F
</syntaxhighlight>
By representing such code in [[bytecode]] format, i.e. as an array of bytes each representing a different instruction, one can make [[Mutation (genetic algorithm)|mutation]] operations simply by changing an element of such an array.