Memetic-Computing
Memetic Computing is a novel computational paradigm that incorporates the notion of meme(s)[1] 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.[2][3][4]
The term memetic computing is often unassumingly misinterpreted to mean the same thing as memetic algorithms (MAs)[5] that typically hybridize population-based global search algorithms with one or more local search schemes. Notably, memetic computing offers a much more broader scope, perpetuating the idea of memes into concepts that pave way towards simultaneous problem learning and optimization approach.
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 recognized instantiations of the memetic computing paradigm are the first-generation memetic algorithms (MAs). 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 (interpreted as computational manifestations of memes) such as heuristic solution refinements, gradient descent procedures, etc. The specific choice of local search heuristics are handcrafted (manually specified) 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 (a pool of memes)[6]; gleaning patterns (knowledge) from the data generated during the course of a search/optimization run so as to ascertain promising combinations of memes at runtime[7][8].
Machine Crafting Memes
It is only recently that the concept of
of memes are set free from the narrow scope human crafted local search heuristics - paving the path towards fully automated meme extraction, dispersal and exploitation.
Applications
The concept of memes have been exploited in various research fields, for example, robotics engineering, multi-agent systems, robotics, optimization[9], software engineering, and the social sciences etc.
See also
References
- ^ Dawkins, R. (1976). The selfish gene. Oxford University Press.
- ^ Ong, Y. S., Lim, M. H., & Chen, X. (2010). Memetic computation—past, present & future [research frontier]. IEEE Computational Intelligence Magazine, 5(2), 24-31.
- ^ Chen, X., Ong, Y. S., Lim, M. H., & Tan, K. C. (2011). A multi-facet survey on memetic computation. IEEE Transactions on Evolutionary Computation, 15(5), 591-607.
- ^ Gupta, A., & Ong, Y. S. (2018). Memetic Computation: The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era (Vol. 21). Springer.
- ^ Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989.
- ^ Krasnogor, N., Blackburne, B. P., Burke, E. K., & Hirst, J. D. (2002, September). Multimeme algorithms for protein structure prediction. In International Conference on Parallel Problem Solving from Nature (pp. 769-778). Springer, Berlin, Heidelberg.
- ^ Krasnogor, N., Blackburne, B. P., Burke, E. K., & Hirst, J. D. (2002, September). Multimeme algorithms for protein structure prediction. In International Conference on Parallel Problem Solving from Nature (pp. 769-778). Springer, Berlin, Heidelberg.
- ^ Ong, Y. S., & Keane, A. J. (2004). Meta-Lamarckian learning in memetic algorithms. IEEE transactions on evolutionary computation, 8(2), 99-110.
- ^ Feng, L., Ong, Y. S., Lim, M. H., & Tsang, I. W. (2015). Memetic search with interdomain learning: A realization between CVRP and CARP. IEEE Transactions on Evolutionary Computation, 19(5), 644-658.