Abstract
This paper proposes an acceleration method of GA search that finds a new elite by fitting a single-peak function on fitness landscape. The roughest approximation of a finite fitness landscape that has one global optimum would be a single-peak curved surface, and the vertex of the approximated single-peak function is expected to be near the global optimum of the original searching space. We propose two data selection methods for the fitting, use a quadratic function as the single-peak function, and evaluate the proposed idea using seven benchmark functions. The experimental results have shown that the proposed method accelerate GA convergence.
