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

Comparative Study on Fitness Landscape Approximation with Fourier Transform

We propose to apply n dimensional discrete Fourier transform (DFT) to a fitness landscape, search an elite individual using obtained principal frequency component and accelerate evolutionary computation (EC) search. A comparative evaluation with our previous works is conducted using eight benchmark functions. The evaluation shows that our proposed approach can obtain the accurate fitness landscape than that with 1 dimensional DFT, and EC acceleration performance can be improved significantly. However, it needs more computational time in the process of conducting n dimensional DFT than that in 1 dimension. We also investigate the computational complexity of the two approaches and some related issues.Ⅰ.INTRODUCTION / Ⅱ.DISCRETE FOURIER TRANSFORM / Ⅲ.APPROXIMATING FITNESS LANDSCAPE BY FOURIER TRANSFORM TO ACCELERATE EVOLUTIONARY SEARCH / Ⅳ.EXPERIMENTAL EVALUATIONS / Ⅴ.DISCUSSION / Ⅵ.CONCLUSION AND FUTURE WORKICGEC 2012 : 2012 Sixth International Conference on Genetic and Evolutionary Computing (ICGEC) : 25-28 August 2012 : Kitakyushu, Japa