Optimized Design Space Exploration Framework Utilizing Predictive Fitness Evaluation Techniques

Abstract

Design space exploration (DSE) is a critical process in system design, allowing for the identification of optimal configurations and parameter settings. This research proposes an advanced framework for DSE that integrates predictive fitness evaluation techniques to enhance the efficiency and accuracy of the exploration process. By leveraging machine learning models to predict the fitness of design points, our approach significantly reduces the computational overhead typically associated with traditional DSE methods. This framework facilitates faster convergence to optimal solutions by guiding the search process more effectively. Empirical results demonstrate that our predictive approach not only accelerates the exploration process but also maintains high accuracy in identifying optimal design configurations. This method has broad applicability across various engineering disciplines, including electronics, aerospace, and mechanical design, where rapid and precise DSE is essential.