Memetic computing
Memetic computing is a novel computational paradigm that incorporates the notion of meme as basic units of transferable information encoded in computational representations for boosting the performance of artificial evolutionary systems in the domain of search and optimization.
The term memetic computing is often unassumingly misinterpreted to mean the same thing as memetic algorithms that typically hybridize population-based global search algorithms with one or more local search schemes. Notably, memetic computing offers a much broader scope, perpetuating the idea of memes into concepts that pave the way towards simultaneous problem learning and optimization approaches.
Methods
There are two different methods that describe the history and rise of memetics in computing. These are human-crafted memes and machine-crafted memes.Human-crafted memes
One of the most widely recognised instantiations of the memetic computing paradigm are the first-generation memetic algorithms. In particular, MAs are referred to as hybrid algorithms, prescribing a marriage between a population-based global search coupled with one or more local search schemes such as heuristic solution refinements, gradient descent procedures, etc. The specific choice of local search heuristics are handcrafted by a domain expert and often require a reasonably deep understanding of the problem at hand.The second generation MAs focus on adaptive data driven selection and integration of memes from a manually specified catalogue of multi-memes ; gleaning patterns from the data generated during the course of a search/optimization run so as to ascertain promising combinations of memes at runtime.