Differential Evolution (DE) is featured by its simple parameter control; genetic operation and fine robustness. However, DE yet still has difficulty with complex functions in continuous space due to its searching blindness and inefficiency from time to time. An adaptive DE algorithm based on LS-SVM (Least Square Support Vector Machine) is proposed in this paper. The key genetic operators such as differential mutation and crossover are modified; Adaptive population evolution guiding strategy based on LS-SVM n-best training set approximation and optimization is designed; With applying condition analyzed, the procedure and complexity of the LS-SVM based evolution guiding strategy is summarized. The comparative results of the proposed DE with traditional one based on various standard test functions effectively demonstrate the high accuracy and efficiency of the proposed approach for continuous multi-modal optimization.

Abstract. Fan-Meshes (FM) are a kind of geometrical primitives for generating 3D model or scene descriptions that are able to preserve both local geometrical details and topological structures. In this paper, we propose an efficient simplification algorithm for the FM models to achieve fast post-processing and rendering of large models or scenes. Given a global error tolerance for the surface approximation, the algorithm can find an approximately minimal set of FMs that covers the whole model surfaces. As compared with splat-based methods, the FM description has a large simplification rate under the same surface fitting error measurement.