  Recently, several algorithms for symbolic regression (SR) emerged which
employ a form of multiple linear regression (LR) to produce generalized linear
models. The use of LR allows the algorithms to create models with relatively
small error right from the beginning of the search; such algorithms are thus
claimed to be (sometimes by orders of magnitude) faster than SR algorithms
based on vanilla genetic programming. However, a systematic comparison of these
algorithms on a common set of problems is still missing. In this paper we
conceptually and experimentally compare several representatives of such
algorithms (GPTIPS, FFX, and EFS). They are applied as off-the-shelf,
ready-to-use techniques, mostly using their default settings. The methods are
compared on several synthetic and real-world SR benchmark problems. Their
performance is also related to the performance of three conventional machine
learning algorithms --- multiple regression, random forests and support vector
regression.
