Fitness landscape analysis of evolved image transforms for defense applications

In recent years, there has been increased interest in the use of evolutionary algorithms (EAs) in the design of robust image transforms for use in defense and security applications. An EA replaces the defining filter coefficients of a discrete wavelet transform (DWT) to provide improved image quality within bandwidth-limited image processing applications, such as the transmission of surveillance data by swarms of unmanned aerial vehicles (UAVs) over shared communication channels. The evolvability of image transform filters depends upon the properties of the underlying fitness landscape traversed by the evolutionary algorithm. The landscape topography determines the ease with which all optimization algorithm may identify highly-fit filters. The properties of a fitness landscape depend upon a chosen evaluation function defined over the space of possible solutions. Evaluation functions appropriate for image filter evolution include mean squared error (MSE), the universal image quality index (UQI), peak signal-to-noise ratio (PSNR), and average absolute pixel error (AAPE). We conduct a theoretical comparison of these image quality measures using random walks through fitness landscapes defined over each evaluation function. This analysis allows us to compare the relative evolvability of the various potential image quality measures by examining fitness topology for each measure in terms of ruggedness and deceptiveness. A theoretical understanding of the topology of fitness landscapes aids in the design of evolutionary algorithms capable of identifying near-optimal image transforms suitable for deployment in defense and security applications of image processing.