Accelerating a GA convergence by fitting a single-peak function
Takeo INGU, Hideyuki Takagi · 1999
This paper proposes an acceleration method of GA search that finds a new elite by fitting a single-peak function on GA search surface. The roughest approximation of a finite searching surface 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 surface. 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 accelerates GA convergence.