Abstract
In this paper we investigate the emergence and power of a complex social system based upon principles of cultural evolution. Cultural Algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. Here we extend the influence and integration function in Cultural Algorithms by adding a mechanism by which knowledge sources can spread their influence throughout a population in the presence of a heterogeneous layered social network. The interaction (overlapping) of the knowledge sources, represented as bounding boxes on the landscape, at the right level projects how efficient the cooperation is between the agents in the resultant “Social Network”. The inter-related structures that emerge with this approach are critical to the effective functioning of the approach. We view these structures as constituting a “normal form” for Cultures within these real-valued optimization landscapes. Our goal will be to identify the minimum social structure needed to solve problems of certain complexities. If this can be accomplished, it means that there will be a correspondence between the social structure and the problem environment in which it emerged. An escalating sequence of complex benchmark problems to our system will be presented. We conclude by suggesting the emergent features are what give cultural systems their power to learn and adapt.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ali M, Reynolds R (2009) An intelligent social fabric influence component in cultural algorithms for knowledge learning in dynamic environments. In: Proceedings of the 2009 IEEE/WIC/ACM international joint conference on Web intelligence and intelligent agent technology, vol 2, September, pp 15–18
Brock W, Durlauf S (2001) Discrete choice with social interactions. Rev Econ Stud 68:235–260
Chung C, Reynolds R (1998) CAEP: an evolution-based tool for real-valued function optimization using cultural algorithms. Int J Artif Intell Tools 7(3):239–291
Coello C, Mezura-Montes E (2002) Handling constraints in genetic algorithms using dominance-based tournaments. In: Parmee I (ed) Proceedings of the fifth international conference on adaptive computing design and manufacture (ACDM 2002), vol 5, University of Exeter, Devon, UK, April. Springer, Berlin, pp 273–284
Deb K, Goldberg D (1989) An investigation of niche and species formation in genetic function optimization. In: Schaffer JD (ed) Proceedings of the third international conference on genetic algorithms, George Mason University, California, June. San Mateo, Morgan Kaufmann, pp 42–50
Deb K, Goyal M (1996) A combined genetic adaptive search genes for engineering design. Comput Sci Inform 26(4):30–45
Horn J, Nafpliotis N, Goldberg D (1994) A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE world congress on computational intelligence, vol 1, Piscataway, NJ, pp 82–87
Hu X, Eberhart C, Shi Y (2003) Engineering optimization with particle swarm. In: Proceedings of the 2003 IEEE swarm intelligence symposium, Indiana, USA. IEEE Press, New York, pp 53–57
Jin X, Reynolds R (1999) Using knowledge-based evolutionary computation to solve nonlinear constraint optimization problems: a cultural algorithm approach. In: Proceeding of the 1999 congress on evolutionary computation, pp 1672–1678
Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation (CEC 1999), Piscataway, NJ, pp 1931–1938
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of the IEEE international conference on neural networks, Perth, Australia, IEEE Service Center, pp 12–13
Liang J, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan P, Coello Coello C, Deb K (2006) Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. Technical report, School of EEE, Nanyang Technological University, Singapore
Mezura-Montes E, Coello Coello C, Landa-Becerra R (2003) Engineering optimization using a simple evolutionary algorithm. In: Proceedings of the 15th IEEE international conference on tools with artificial intelligence, 3–5 November, p 149
Morrison R, Jong K (1999) A test problem generator for non-stationary environments. In: Proceedings of congress on evolutionary computation. IEEE Press, New York, pp 2047–2053
Peng B, Reynolds R, Brewster J (2003) Cultural swarms. In: Proceeding of IEEE 2003 congress on evolutionary computation. IEEE Press, New York
Reynolds R (1978) On modeling the evolution of hunter-gatherer decision-making systems. Geogr Anal 10(1):31–46
Reynolds R, Peng B (2005) Cultural algorithms: computational modeling of how cultures learn to solve problems: an engineering example. Cybern Syst 36(8):753–771
Reynolds R, Saleem S (2003) The impact of environmental dynamics on cultural emergence. Festschrift in honor of John Holland, Oxford University Press, pp 1–10
Rychlyckyj N, Ostrowski D, Schleis G, Reynolds R (2003) Using cultural algorithms in industry. In: Proceeding of the 2003 IEEE swarm intelligence symposium, pp 187–192
Acknowledgements
This research has been supported partially by the higher council of science & technology, IT sector, Jordan, 2009.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ali, M.Z., Salhieh, A., Abu Snanieh, R.T. et al. Boosting cultural algorithms with a heterogeneous layered social fabric influence function. Comput Math Organ Theory 18, 193–210 (2012). https://doi.org/10.1007/s10588-012-9116-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10588-012-9116-z