Micro-differential evolution: diversity enhancement and comparative study.

Evolutionary algorithms (EAs), such as the differential evolution (DE) algorithm, suffer from high computational time due to large population size and nature of evaluation, to mention two major reasons. The micro-EAs employ a very small population size, which can converge to a reasonable solution quicker; while they are vulnerable to premature convergence as well as high risk of stagnation. One approach to overcome the stagnation problem is increasing the diversity of the population. In this thesis, a micro-differential evolution algorithm with vectorized random mutation factor (MDEVM) is proposed, which utilizes the small size population benefit while preventing stagnation through diversification of the population. The following contributions are conducted related to the micro-DE (MDE) algorithms in this thesis: providing Monte-Carlo-based simulations for the proposed vectorized random mutation factor (VRMF) method; proposing mutation schemes for DE algorithm with populations sizes less than four; comprehensive comparative simulations and analysis on performance of the MDE algorithms over variant mutation schemes, population sizes, problem types (i.e. uni-modal, multi-modal, and composite), problem dimensionalities, mutation factor ranges, and population diversity analysis in stagnation and trapping in local optimum schemes. The comparative studies are conducted on the 28 benchmark functions provided at the IEEE congress on evolutionary computation 2013 (CEC-2013) and comprehensive analyses are provided. Experimental results demonstrate high performance and convergence speed of the proposed MDEVM algorithm over variant types of functions

Image Augmentation using Radial Transform for Training Deep Neural Networks

Deep learning models have a large number of free parameters that must be estimated by efficient training of the models on a large number of training data samples to increase their generalization performance. In real-world applications, the data available to train these networks is often limited or imbalanced. We propose a sampling method based on the radial transform in a polar coordinate system for image augmentation to facilitate the training of deep learning models from limited source data. This pixel-wise transform provides representations of the original image in the polar coordinate system by generating a new image from each pixel. This technique can generate radial transformed images up to the number of pixels in the original image to increase the diversity of poorly represented image classes. Our experiments show improved generalization performance in training deep convolutional neural networks with radial transformed images.Comment: This paper is accepted for presentation at IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE ICASSP), 201