Social Evolution: An Evolutionary Algorithm Inspired by Human Interactions

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


Inherent intelligent characteristics of humans, such as human interactions and information exchanges enable them to evolve more rapidly than any other species on the earth. Human interactions are generally selective and are free to explore randomly based on the individual bias. When the interactions are indecisive, individuals consult for second opinion to further evaluate the indecisive interaction before adopting the change to emerge and evolve. Inspired by such human properties, in this paper a novel social evolution (SE) algorithm is proposed and tested on four numerical test functions to ascertain the performance by comparing the results with the state-of-the-art soft computing techniques on standard performance metrics. The results indicate that, the performance of SE algorithm is better than or quite comparable to the state-of-the-art nature inspired algorithms.


Society and civilization Social evolution optimization 



Authors gratefully acknowledge the inspiration and guidance of the Most Revered Prof. P. S. Satsangi, the Chairman, Advisory Committee on Education, Dayalbagh, Agra, India.


  1. 1.
    Yoshida, Z.: Nonlinear Science: the Challenge of Complex Systems. Springer, Heidelberg (2010)Google Scholar
  2. 2.
    de Castro L.N.: Fundamentals of natural computing: an overview. Phys. Life Rev. 4(1), 1–36 (2007)Google Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Kluwer Academic Publishers, Boston, MA (1989)MATHGoogle Scholar
  4. 4.
    Fogel, D.B.: Evolutionary computation: toward a new philosophy of machine intelligence (3rd edn). IEEE Press, Piscataway, NJ (2006)Google Scholar
  5. 5.
    Beyer, H.-G., Schwefel, H.-P.: Evolution strategies: a comprehensive introduction. J. Nat. Comput. 1(1), 3–52 (2002)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, Heidelberg (1998)Google Scholar
  7. 7.
    Korns, Michael: Abstract Expression Grammar Symbolic Regression, in Genetic Programming Theory and Practice VIII. Springer, New York (2010)Google Scholar
  8. 8.
    White, T., Pagurek, B.: Towards multi-swarm problem solving in networks, In: Proceedings of the 3rd International Conference on Multi-Agent Systems (ICMAS-98), pp. 333–40, (1998)Google Scholar
  9. 9.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In Proceedings of 1995 IEEE International Conference Neural Networks IV, pp. 1942–1948, (1995)Google Scholar
  10. 10.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Reynolds, R.G.: An introduction to cultural algorithms. In: Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 131–139. San Diego, California (1994)Google Scholar
  12. 12.
    Reynolds, R.G., Peng, B., Brewster, J.J.: Cultural swarms: knowledge-driven problem solving in social systems. IEEE Int. Conf. Syst. Man Cybern. 4, 3589–3594 (2003)Google Scholar
  13. 13.
    Reynolds, R.G., Peng, B., Brewster, J.: Cultural swarms. Congr. Evol. Comput. 3, 1965–1971 (2003A)Google Scholar
  14. 14.
    Reynolds, R.G., Jacoban, R., Brewster, J.: Cultural swarms: assessing the impact of culture on social interaction and problem solving. In: Proceedings of the 2003 IEEE Swarm Intelligence, Symposium, pp. 212–219 (2003b)Google Scholar
  15. 15.
    Reynolds, RG, Kobti, Z., Kohler, T.: The effect of culture on the resilience of social systems in the village multi-agent simulation. In: Proceedings of IEEE International Congress on Evolutionary Computation. Portland, OR, vol. 24, pp. 1743–1750, June 19 (2004)Google Scholar
  16. 16.
    Reynolds, R.G., Whallon, R., Mostafa, Z.A., Zadegan, B.M.: Agent-based modeling of early cultural evolution. IEEE Congress on Evolutionary Computation. pp. 1135–1142 (2006)Google Scholar
  17. 17.
    Ray, T., Liew, K.M.: Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans. Evol. Comput. 7(4), 386–396 (2003)Google Scholar
  18. 18.
    Akay B., Karaboga D.: Artificial bee colony algorithm for large-scale problems and engineering design optimization. J. Intel. Manuf. pp. 1–14 (2010). DOI: 10.1007/s10845-010-0393-4Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Dayalbagh Educational InstituteDayalbaghIndia

Personalised recommendations