Bell-Curve Based Evolutionary Optimization Algorithm


The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.

9 Figures and Tables

Cite this paper

@inproceedings{SobieszczanskiSobieski2004BellCurveBE, title={Bell-Curve Based Evolutionary Optimization Algorithm}, author={Jaroslaw Sobieszczanski-Sobieski and Keith E. Laba and Rex K. Kincaid}, year={2004} }