  Endmember Extraction is a critical step in hyperspectral image analysis and
classification. It is an useful method to decompose a mixed spectrum into a
collection of spectra and their corresponding proportions. In this paper, we
solve a linear endmember extraction problem as an evolutionary optimization
task, maximizing the Simplex Volume in the endmember space. We propose a
standard genetic algorithm and a variation with In Vitro Fertilization module
(IVFm) to find the best solutions and compare the results with the state-of-art
Vertex Component Analysis (VCA) method and the traditional algorithms Pixel
Purity Index (PPI) and N-FINDR. The experimental results on real and synthetic
hyperspectral data confirms the overcome in performance and accuracy of the
proposed approaches over the mentioned algorithms.
