Accelerating evolutionary computation with elite obtained in projected one-dimensional spaces

We propose a method for accelerating evolutionary computation (EC) searches using an elite obtained in one-dimensional space and use benchmark functions to evaluate the proposed method. The method projects individuals onto n one-dimensional spaces corresponding to each of the n searching parameter axes, approximates each landscape using Lagrange polynomial interpolation or power function least squares approximation, finds the best coordinate for the approximated shape, obtains an elite by combining the best n found coordinates, and uses the elite for the next generation of the EC. The advantage of this method is that the elite may be easily obtained thanks to their projection onto each one-dimensional space and there is a higher possibility that the elite will be located near the global optimum. Experimental tests with differential evolution and eight benchmark functions show that the proposed method accelerates EC convergence significantly, especially in early generations.