Applications of Artificial Intelligence (AI) are rapidly
increasing especially in the infrastructure of every
industry, and researchers continually try to develop new
efficient AI algorithms or improve the current ones to
maximize their benefits. In this paper, we introduce a new
hybrid evolutionary algorithm, called the Memetic
Programming (MP) algorithm, that hybridizes the Genetic
Programming (GP) algorithm with a new set of local search
procedures over a tree space. Specifically, in each
generation of the MP algorithm, we use the GP strategy to
generate a new population. Then, using some local search
procedures over a tree space, we try to improve promising
programs from the generated population. In addition, the
MP algorithm can deal with the Automatically Defined
Function (ADF) technique that enables the algorithm to
exploit the modularities in problem environments. Through
extensive numerical experiments, the proposed MP algorithm
is shown to have promising performance compared to some
recent versions of the GP algorithm, especially by using
the ADF technique.