Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A two-dimensional interpolation function for irregularly-spaced data
ACM '68 Proceedings of the 1968 23rd ACM national conference
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Hierarchically organised evolution strategies on the parabolic ridge
Proceedings of the 8th annual conference on Genetic and evolutionary computation
ICDCS '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems
Evolutionary algorithms and gradient search: similarities anddifferences
IEEE Transactions on Evolutionary Computation
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This paper proposes a novel recombination scheme for evolutionary algorithms, which can guide the new population generation towards the maximum increase of the objective function. Given the current sample points and their function evaluations, the Shepard's interpolation method is used to approximate the underlying objective function in that local region. We then compute the gradient of the estimated function which in consequence leads to an iterative process, called the mean shift, for searching the local function optimum. In each mean shift step, we calculate the weighted mean of sample points in the kernel window, followed by shifting the location of the kernel to the computed mean. Such iterative process eventually converges to the point at which the estimated objective function has zero gradient. We use the converged point as the output of our recombination operator. Experimental results show that such gradient based recombination scheme can improve the efficiency of optimization search in evolutionary algorithms.