Comparative Evaluations of Evolutionary Computation with Elite Obtained in Reduced Dimensional Spaces
Comparative Evaluations of Evolutionary Computation with Elite Obtained in Reduced Dimensional Spaces
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DOI：
10.1109/incos.2011.66
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Yan Pei;H. Takagi
中科院分区：
文献类型：
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作者：
Yan Pei;H. Takagi
我们提出了一个精英合成优化策略，加速进化计算（EC）的搜索使用精英从低维空间。该方法将个体投影到与$n$搜索参数轴中的每一个相对应的$n$一维空间上，使用拉格朗日多项式插值或幂函数最小二乘逼近来近似每个景观，找到近似形状的最佳坐标，通过组合最佳$n$找到的坐标来获得精英，并且将精英用于下一代EC。该方法的优点在于，由于精英在每个一维空间上的投影，可以容易地获得精英，并且精英将位于全局最优值附近的可能性更高。我们进行实验测试，比较我们提出的方法与以前的加速方法，使用差分进化和10个基准函数。结果表明，所提出的方法加快EC收敛显着，特别是在早期代。
We propose an elite synthesis optimization strategy for accelerating evolutionary computation (EC) searches using elites obtained from a lower dimensional 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 power function least squares approximation, finds the best coordinate for the approximated shape, obtains the 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 that there is a higher possibility that the elite will be located near the global optimum. We conduct experimental tests to compare our proposed approaches with previous acceleration approaches using differential evolution and ten benchmark functions. The results demonstrate that the proposed method accelerates EC convergence significantly, especially in early generations.