The adaptive memory algorithm (AMA) is a population-based metaheuristics initially developed in 1995 by Rochat and Taillard. AMA relies on a central memory M and consists in three steps: generate a new solution s from M with a recombination operator, improve s with a local search operator, and use s to update M with a memory update operator. In 1999, Galinier and Hao proposed the GPX recombination operator for the graph coloring problem. In this paper, AMC, a general type of evolutionary algorithm, is formalized and called Adaptive Memory Algorithm with the Covering Recombination Operator. It relies on a specific formulation of the considered problem and on a generalization of the GPX recombination operator. It will be showed that AMC has obtained promising results in various domains, such as graph coloring, satellite range scheduling and project management.