  This paper proposes a novel evolutionary algorithm called Epistocracy which
incorporates human socio-political behavior and intelligence to solve complex
optimization problems. The inspiration of the Epistocracy algorithm originates
from a political regime where educated people have more voting power than the
uneducated or less educated. The algorithm is a self-adaptive, and
multi-population optimizer in which the evolution process takes place in
parallel for many populations led by a council of leaders. To avoid stagnation
in poor local optima and to prevent a premature convergence, the algorithm
employs multiple mechanisms such as dynamic and adaptive leadership based on
gravitational force, dynamic population allocation and diversification,
variance-based step-size determination, and regression-based leadership
adjustment. The algorithm uses a stratified sampling method called Latin
Hypercube Sampling (LHS) to distribute the initial population more evenly for
exploration of the search space and exploitation of the accumulated knowledge.
To investigate the performance and evaluate the reliability of the algorithm,
we have used a set of multimodal benchmark functions, and then applied the
algorithm to the MNIST dataset to further verify the accuracy, scalability, and
robustness of the algorithm. Experimental results show that the Epistocracy
algorithm outperforms the tested state-of-the-art evolutionary and swarm
intelligence algorithms in terms of performance, precision, and convergence.
