Mining medical data images have great potential for exploring hidden patterns in the medical domain. Medical data are heterogeneous which involves images to a great extent like MRI, ECG or Stroke effects etc. Knowledge discovery from such data can improve the diagnostic technique. However, to make the machine learn from such datasets requires large data. In the low-data regime, machine learning algorithms work poorly. Data Augmentation alleviates this by using existing data more effectively, but standard data augmentation produces only limited alternative data. Recent developments in Deep Learning field is noteworthy when it comes to learning probability distribution of points through neural networks, and one of key part for such progress is because of Generative Adversarial Networks(GANs). In this paper, we propose an evolutionary training technique using a cultural algorithm(CA) for neuro-evolution of deep task oriented GANs to find the best architecture for the given dataset. This architecture will help in generating similar but completely new data images which can be further used for training diagnostic Neural Networks. We have compared our approach with the Genetic Algorithm(GA) based neuro-evolution of GANs and show that CA based neuro-evolution of GANs evolves architecture which can generate a higher number of stroke-face images with better resolution when there is low data of original stroke faces.

Introspective reasoning can enable a reasoner to learn by refining its own reasoning processes. In order to perform this learning, the system must monitor the course of its reasoning to detect learning opportunities and then apply appropriate learning strategies. This article describes lessons learned from research on a computer model of how introspective reasoning can guide failure-driven learning. The computer model monitors its own reasoning by comparing it to a model of the desired behaviour of its reasoning, and learns in response to deviations from the ideal defined by the model. The approach is applied to the problem of determining indices for selecting cases from a case-based planner's memory. Experiments show that learning driven by this introspective reasoning both decreases retrieval effort and improves the quality of plans retrieved, increasing the overall performance of the planning system compared to case learning alone.