Variance Reduction in Population-Based Optimization: Application to Unit Commitment

forthcomingInternational audienceWe consider noisy optimization and some traditional variance reduction techniques aimed at improving the convergence rate, namely (i) common random numbers (CRN), which is relevant for population-based noisy optimization and (ii) stratified sampling, which is relevant for most noisy optimization problems. We present artificial models of noise for which common random numbers are very efficient, and artificial models of noise for which common random numbers are detrimental. We then experiment on a desperately expensive unit commitment problem. As expected, stratified sampling is never detrimental. Nonetheless, in practice, common random numbers provided, by far, most of the improvement

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

Development of an advanced artificial intelligent reliability analysis tool to enhance ship operations and maintenance activities

No Abstract availableNo Abstract availabl

Paired Comparison-based Interactive Differential Evolution

We propose a system of Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User interface and convergence performance are central to reducing Interactive Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, users of the proposed IDE and tournament IGA do not need to compare whole individuals with each other but rather only to compare pairs of individuals, which largely decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate another factor, IEC convergence performance, using IEC simulators and show that our proposed IDE converges significantly faster than IGA and tournament IGA, i.e. our proposed method is superior to others from both user interface and convergence performance points of view.Ⅰ.INTRODUCTION / Ⅱ.EC ALGORITHMS / Ⅲ.EVALUATION TASK / Ⅳ.EXPERIMENTAL RESULTS / Ⅴ.DISCUSSION / Ⅵ.CONCLUSION2009 World Congress on Nature & Biologically Inspired Computing : 9 – 11 December 2009 : Coimbatore, Indi

Paired Comparison-based Interactive Differential Evolution for Cochlear Implant Fitting

第6回進化計算学会研究会 : 2014年3月6-7日　東

Emotional Expressions of Vibrotactile Haptic Message Designed by Paired Comparison-based Interactive Differential Evolution

進化計算シンポジウム2011 : 2011年12月17-18日 : 宮

Comparing paired comparison-based interactive DE and tournament interactive GA on stained glass design

Tournament Interactive Genetic Algorithm (T-IGA) and Paired Comparison-based Interactive Differential Evolution (PC-IDE) are applied to the design of stained glass windows and the two algorithms with variable length genotype are compared in a context of interactive evolutionary computation. For both methods, stained glass windows are represented by colored 2D Voronoi diagrams, and a specific phenotypic crossover operator allows offspring to inherit visual features from both parents. The two algorithms have been evaluated by two professional stained-glass artists whom use them to create original designs in a controlled experimental setting. The results indicate superiority of PC-IDE, thus confirming previous theoretical results