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Thetermmemeticalgorithms [1](MAs) wasintroducedinthelate1980stodenoteafamilyofmetaheuristics 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 was engaged in periods of individual improvement of the solutions while they interact sporadically. Another main objective theme was to introduce problem and instance-dependent knowledge as a way of speedingup the search process. Initially, hybridizations included Evolutionary Algorithms (EAs)[2–5], Simulated Annealing and its variants [6, 7], and Tabu Search [8, 9]. Today, a number of hybridizations include other metaheuristics [10] as well as exact algorithms, in complete anytime memetic algorithms [11]. These methods not only prove optimality, but can also deliver high-quality solutions early on in the process.

The adjective “memetic” comes from the term “meme,” coined by R. Dawkins [12], to denote a term analogous to the gene in the context of cultural evolution. It was first proposed as a means of conveying the message that, although inspiring for many, biological evolution should not constrain the imagination to develop population-based methods. Other forms of evolution may be faster, with cultural evolution being one of those less-restrictive examples.