Memetic Algorithms

Abstract

The term ‘Memetic Algorithms’ [74] (MAs) was introduced in the late 80s to denote a family of metaheuristics that have as central theme the hybridization of different algorithmic approaches for a given problem. Special emphasis was given to the use of a population-based approach in which a set of cooperating and competing agents were engaged in periods of individual improvement of the solutions while they sporadically interact. Another main theme was to introduce problem and instance-dependent knowledge as a way of speeding-up the search process. Initially, hybridizations included Evolutionary Algorithms –EAs [35, 41, 89, 97], Simulated Annealing and its variants [52] [79] and Tabu Search [75] [9]. Today, a number of hybridizations include other metaheuristics [42] as well as exact algorithms, in complete anytime memetic algorithms [76]. These methods not only prove optimality, they can deliver high-quality solutions early on in the process. The adjective ‘memetic’ comes from the term ’meme’, coined by R. Dawkins [30] to denote an analogous to the gene in the context of cultural evolution. It was first proposed as a mean of conveying the message that, although inspiring for many, biological evolution should not constrain the imagination to develop