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| 概要 |
We propose an elitism collaborative optimization strategy for accelerating evolutionary computation (EC) searches using elite obtained in reduced dimension space. 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 a linear function approximation by a least square method, finds the best coordinate for the approximated shape, obtains elite by combining the best n found coordinates, and uses the elite to accelerate EC in the next generation. 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. We conduct the experimental tests to compare our proposed approaches with previous acceleration approaches by differential evolution and ten benchmark functions, the results show that the proposed method accelerates EC convergence significantly, especially in early generations.続きを見る
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