Inspired by Darwin’s theory of evolution, the genetic algorithm (GA) method is part of evolutionary computing. It is a search technique used to find solutions and optimize them. This method has found application in different fields, such as computer science, engineering, chemistry, economics, physics, and mathematics. In the present study, GA is used to optimize airfoil geometry for high lift in the low-speed subsonic regime. The variable to be optimized is the set of coordinates of several points along the airfoil surface, which constructs its geometry. We seek a geometrical design that maximizes the fitness function (also called objective function), which is chosen to be the lift coefficient. The process is done in successive cycles, until a satisfactory design is achieved. At the end of each cycle, a group (or a generation) of candidate designs, is generated using stochastic searching. The method involves binary encoding-decoding and mutating as well. An aerodynamic flow solver is augmented in the GA procedure; it evaluates the fitness function at each cycle. A special procedure in evaluating the fitness function is used so that impractical geometrical designs are eliminated automatically.

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