Memetic algorithms are the subject of intense scientific research (a scientific journal devoted to their research is going to be launched) and have been successfully applied to a multitude of real-world problems. Although many people employ techniques closely related to memetic algorithms, alternative names such as hybrid genetic algorithms are also employed. Furthermore, many people term their memetic techniques as genetic algorithms. The widespread use of this misnomer hampers the assessment of the total amount of applications.

Researchers have used memetic algorithms to tackle many classical NP problems. To cite some of them: graph partitioning, multidimensional knapsack, travelling salesman problem, quadratic assignment problem, set cover problem, minimal graph colouring, max independent set problem, bin packing problem and generalized assignment problem.

More recent applications include (but are not limited to): training of artificial neural networks[14], pattern recognition[15], robotic motion planning[16], beam orientation[17], circuit design[18], electric service restoration[19], medical expert systems[20], single machine scheduling[21], automatic timetabling (notably, the timetable for the NHL [22]), manpower scheduling [23], nurse rostering and function optimisation[24], processor allocation[25], maintenance scheduling (for example, of an electric distribution network[26]), VLSI design[27], clustering of gene expression profiles[28], feature/gene selection [29][30] and multi-class, multi-objective feature selection [31]

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