From the introduction: The generic denomination of ‘Memetic Algorithms’ (MAs) is used to encompass a broad class of metaheuristics (i.e., general purpose methods aimed to guide an underlying heuristic). The method is based on a population of agents and proved to be of practical success in a variety of problem domains and in particular for the approximate solution of \({\mathcal{NP}}\) optimization problems. Unlike traditional Evolutionary Computation (EC) methods, MAs are intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature that characterizes MAs. This functioning philosophy is perfectly illustrated by the term “memetic”. Coined by Dawkins, the word ‘meme’ denotes an analogous to the gene in the context of cultural evolution. In Albeit unfortunately under different names. MAs have become an important optimization approach, with several succeses in a variety of classical \({\mathcal{NP}}\) optimization problems.
We aim to provide an updated and self-contained introduction to MAs, focusing on their technical innards and formal features, but without loosing the perspective of their practical application and open research issues. The bibliography contains 231 entries.
For the entire collection see [Zbl 1058.90002].