Improving EA-based Design Space Exploration by Utilizing Symbolic Feasibility Tests

This paper will propose a novel approach in combining Evolutionary Algorithms with symbolic techniques in order to improve the convergence of the algorithm in the presence of large search spaces containing only few feasible solutions. Such problems can be encountered in many real-world applications. Here, we will use the example of design space exploration of embedded systems to illustrate the benefits of our approach. The main idea is to integrate symbolic techniques into the Evolutionary Algorithm to guide the search towards the feasible region. We will present experimental results showing the advantages of our novel approach