From a computing perspective, a meme denotes information that represents knowledge, patterns, rules, or strategies used to solve complex problems. When applied on a problem, memes help a solver to arrive at good quality solutions more efficiently, guiding the search process according to certain procedures or rules, instead of randomly searching through the solution space. Depending on the complexity of the problems, evaluating the suitability of memes and selecting a set of effective memes for different problems, however, are not straightforward tasks. A meme that works well for some problems may not be effective for other problems. Besides, different memes might have different degrees of importance in solving a problem. The level of importance of each meme might also change at different stages of the search. In this paper, we discuss how multiple memes can be generated and applied to solve computational optimization problems. A case study in combinatorial optimization is also presented and discussed.
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