Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization

A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). The fitness landscape of human subjective evaluation in IEC is very difficult and impossible to model, even with a hypothesis of what its definition might be. In this paper, we propose a method to establish a human model in projected high dimensional search space by kernel classification for enhancing IEC search. Because bivalent logic is a simplest perceptual paradigm, the human model is established by considering this paradigm principle. In feature space, we design a linear classifier as a human model to obtain user preference knowledge, which cannot be supported linearly in original discrete search space. The human model is established by this method for predicting potential perceptual knowledge of human. With the human model, we design an evolution control method to enhance IEC search. From experimental evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search significantly

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 onedimensional 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.Ⅰ.INTRODUCTION / Ⅱ.OBTAINING ELITE FROM A REGRESSION SEARCH SPACE / Ⅲ.EXPERIMENTAL EVALUATIONS / Ⅳ.DISCUSSIONS / Ⅴ.CONCLUSION2011 Fifth International Conference on Genetic and Evolutionary Computing ICGEC 2011 : 29 August - 1 September 2011 : Kinmen, Taiwan/Xiamen, Chin