MA|PM - Memetic Algorithms with Population Management

Many researchers agree that the quality of a metaheuristic optimization approach is largely a result of the interplay between intensification and diversification strategies (see e.g. Ferland et al. (2001); Laguna et al. (1999)). One of the main motivations for this paper is the observation that the design of evolutionary algorithms, including memetic algorithms, makes it particularly difficult to control the balance between intensification and diversification. As Hertz and Widmer (2003) point out, preserving the diversity of the population of an evolutionary algorithm is necessary. Although EA have the operators to increase or decrease the diversity of the population, most lack the means to control this diversification. Using diversity measures in genetic algorithms is not a new idea, and has been proposed in the context of fitness sharing, crowding and many others. MA|PM differs from these methods in several respects, the most important ones being the maintenance of a small population of high-quality individuals and the use of population management strategies to actively control the diversity.