abstract = "Differential Evolution (DE) is an evolutionary
heuristic for continuous optimisation problems. In DE,
solutions are coded as vectors of floats that evolve by
crossover with a combination of best and random
individuals from the current generation. Experiments to
apply DE to automatic programming were made recently by
Veenhuis, coding full program trees as vectors of
floats (Tree Based Differential Evolution or TreeDE).
In this paper, we use DE to evolve linear sequences of
imperative instructions, which we call Linear
Differential Evolutionary Programming (LDEP). Unlike
TreeDE, our heuristic provides constant management for
regression problems and lessens the tree-depth
constraint on the architecture of solutions.
Comparisons with TreeDE and GP show that LDEP is
appropriate to automatic programming.",