基于进化策略的CHC遗传算法及岩性波谱识别

野外实测岩性波谱数据的数据挖掘可以为高光谱遥感建模提供依据.针对实测波谱数据的特点, 设计了一种基于Monte Carlo抽样进化机制的CHC (cross generation elitist selection, heterogeneous recombination, cataclysmic mutation , 跨世代精英选拔、异物种重组、灾变变异) 遗传算法用于多类岩性判别.应用于云南北衙金矿蚀变岩的识别, 表明该方法具有快速高效性. 

An Evolutionary-Strategy-Based CHC Genetic Algorithm and Its Application to Rock Spectrum Discrimination

Data mining from field rock spectrums is important for hyper-spectral remote sensing modeling. The characteristics of the field spectrum data are used to reform and combine evolutionary strategies with CHC (cross generation elitist selection, heterogeneous recombination and cataclysm mutation) and to design a genetic algorithm to conduct rock spectrum discrimination: to build a linear discriminating equation with many variables (wavelength intervals). Floating encoding is used for the coefficients of the equation (genes). Two optimization objectives are compared with each other: one is to maximize the right-judgment ratio of known samples, and the other is to minimize the ratio of in-class versus between-class distances. The results show that the former is briefer and faster while both of them are effective. Monte Carlo sampling is employed to adjust searching spaces. Uniform and Gaussian distribution models are employed to produce new-generation genes, showing that the former model has better performance, because the genes have various unknown distributions. An experimental study is presented based on the data of 1 823 wavelength intervals from Beiya gold deposit, Yunnan, China, obtained with the FieldSpectr Fr equipment (ASD Co. US). The algorithm proves high efficiency in identification of altered dolomitic limestone, a potentially gold-bearing rock, from other rocks.