Fluid Genetic Algorithm (FGA)

Abstract

AbstractGenetic Algorithm (GA) has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive. A review of the literature shows extensive research attempting to adapt and develop the standard GA. Nevertheless, the essence of GA which consists of concepts such as chromosomes, individuals, crossover, mutation, and others rarely has been the focus of recent researchers. In this paper method, Fluid Genetic Algorithm (FGA), some of these concepts are changed, removed, and furthermore, new concepts are introduced. The performance of GA and FGA are compared through seven benchmark functions. FGA not only shows a better success rate and better convergence control, but it can be applied to a wider range of problems including multi-objective and multi-level problems. Also, the application of FGA for a real engineering problem, Quadric Assignment Problem (AQP), is shown and experienced.Highlights This work presents a novel Genetic Algorithm alteration. Chromosome concept and structure in FGA is more similar to the real genetic world. FGA comprises global and individual learning rates. We show FGA enjoys higher success rate, and better convergence control.