Abstract
The word meme was coined in a sociological context by Richard Dawkins in his 1976 book The Selfish Gene. Drawing an analogy to our understanding of genes as basic units of biological heredity, the concept of memes was introduced for representing basic units of cultural information transfer. In other words, the new science of memetics serves as a means of explaining the propagation of information through and across populations, leading to the proliferation of ideas, catch-phrases, fashions, behavioral patterns, etc., based on principles similar to that of Darwinian evolution. Indeed, genetics combined with the notion of memes provides a way to understand the biological evolution of populations in conjunction with their observed behavioral and cultural traits. Interestingly, the implications of the underlying principles are not merely restricted to the realm of sociology and evolutionary biology, but have also penetrated the field of computer science, particularly enriching the nature-inspired subfield of computational intelligence. However, it is worth noting that while algorithms mimicking facets of genetic evolution have been around for several decades, it is still early days for memetics in this regard.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dawkins, R. (1976). The selfish gene. Oxford University Press.
Engelbrecht, A. P. (2007). Computational intelligence: An introduction. New York: Wiley.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, 1989. Reading: Addison-Wesley.
Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection (Vol. 1). MIT Press.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16). New York: Wiley.
Gilli, M., & Schumann, E. (2014). Optimization cultures. Wiley Interdisciplinary Reviews: Computational Statistics, 6(5), 352–358.
Spencer, H. (1864). The principles of biology (Vols. 2) London: Williams and Norgate. (System of synthetic philosophy, 2).
Darwin, C. (1859). On the origin of species.
Rechenberg, I. (1994). Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. frommann-holzbog, Stuttgart, 1973.
Schwefel, H. P. (1977). Numerische optimierung von computer-modellen mittels der evolutionsstrategie (Vol. 1). Switzerland: Birkhäuser, Basel.
Altenberg, L. (1995). The schema theorem and Price’s theorem. Foundations of Genetic Algorithms, 3, 23–49.
Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39.
Kennedy, J. (2011). Particle swarm optimization. In Encyclopedia of machine learning (pp. 760–766). Springer US.
Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.
Tayarani-N, M. H., Yao, X., & Xu, H. (2015). Meta-heuristic algorithms in car engine design: a literature survey. IEEE Transactions on Evolutionary Computation, 19(5), 609–629.
Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826.
Moscato, P., & Cotta, C. (2010). A modern introduction to memetic algorithms. In Handbook of metaheuristics (pp. 141–183). Springer US.
Lim, D., Ong, Y. S., Gupta, A., Goh, C. K., & Dutta, P. S. (2016). Towards a new Praxis in optinformatics targeting knowledge re-use in evolutionary computation: simultaneous problem learning and optimization. Evolutionary Intelligence, 9(4), 203–220.
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.
Ong, Y. S., Lim, M. H., & Chen, X. (2010). Memetic computation—past, present & future (research frontier). IEEE Computational Intelligence Magazine, 5(2), 24–31.
Kellerer, H., Pferschy, U., & Pisinger, D. (2004). Introduction to NP-completeness of Knapsack problems. In Knapsack problems (pp. 483–493). Berlin, Heidelberg: Springer.
Krasnogor, N., Blackburne, B. P., Burke, E. K., & Hirst, J. D. (2002, September). Multimeme algorithms for protein structure prediction. In PPSN (pp. 769–778).
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.
Ong, Y. S., & Keane, A. J. (2004). Meta-Lamarckian learning in memetic algorithms. IEEE Transactions on Evolutionary Computation, 8(2), 99–110.
Le, M. N., Ong, Y. S., Jin, Y., & Sendhoff, B. (2012). A unified framework for symbiosis of evolutionary mechanisms with application to water clusters potential model design. IEEE Computational Intelligence Magazine, 7(1), 20–35.
Chen, X., & Ong, Y. S. (2012). A conceptual modeling of meme complexes in stochastic search. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(5), 612–625.
Zhou, Z., Ong, Y. S., Lim, M. H., & Lee, B. S. (2007). Memetic algorithm using multi-surrogates for computationally expensive optimization problems. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 11(10), 957–971.
Gupta, A., Ong, Y. S., & Feng, L. (2018). Insights on transfer optimization: Because experience is the best teacher. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 51–64.
Min, A. T. W., Ong, Y. S., Gupta, A., & Goh, C. K. (2017). Multi-problem surrogates: Transfer evolutionary multiobjective optimization of computationally expensive problems. IEEE Transactions on Evolutionary Computing.
Gupta, A., Ong, Y. S., & Feng, L. (2017). Multifactorial evolution: toward evolutionary multitasking. IEEE Transactions on Emerging Topics in Computational Intelligence.
Bonyadi, M. R., Michalewicz, Z., Neumann, F., & Wagner, M. (2016). Evolutionary computation for multicomponent problems: Opportunities and future directions. arXiv preprint arXiv:1606.06818.
Feng, L., Gupta, A., & Ong, Y. S. (2017). Compressed representation for higher-level meme space evolution: A case study on big knapsack problems. Memetic Computing, 1–15.
Hodgson, G. M. (2005). Generalizing Darwinism to social evolution: Some early attempts. Journal of Economic Issues, 39(4), 899–914.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Gupta, A., Ong, YS. (2019). Introduction: The Rise of Memetics in Computing. In: Memetic Computation. Adaptation, Learning, and Optimization, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-02729-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-02729-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02728-5
Online ISBN: 978-3-030-02729-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)