Social evolution: An evolutionary algorithm inspired by human interactions

Abstract

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.