Abstract: The present invention relates to novel systems and methods in which a machine learning mode is used to generate new populations in an evolutionary process. A preferred embodiment of the present invention called Learnable Evolution Model (briefly, LEM) employs a machine learning mode at selected steps of evolutionary computation to determine reasons why certain individuals in a population are superior to others in performing a designated class of tasks. These reasons, expressed as inductive hypotheses, are used to create a new generation of individuals (phenotypes or genotypes). An evolutionary process in LEM can alternate between a machine learning mode and a Darwinian evolution mode switching to another mode, in any effective order, when a mode termination condition is met, or it can rely on a repetitious application of the machine learning mode with randomly or methodologically generated populations.