An adaptive prioritized ε -preferred evolutionary algorithm for approximate BDD optimization
Saeideh Shirinzadeh, Mathias Soeken, Daniel J. Grosse, Rolf Drechsler · Proceedings of the Genetic and Evolutionary Computation Conference · 2017
Approximate computing is an emerging methodology that allows to increase efficiency in a range of resilient applications for an affordable loss of precision or quality. In this paper, we exploit approximation in a multi-criteria optimization approach for the widely used data structure Binary Decision Diagram (BDD) to achieve higher efficiency besides lowering the inaccuracy. For this purpose, we utilize an ε-preferred evolutionary algorithm giving a higher priority to minimize BDD sizes as well as maintaining certain error constraints. In particular, we propose an adaptive ε-setting method which adds an automated factor to the algorithm based on the behavior of the function under approximation. This improves the performances of the algorithm by correcting the effect of the user set error constraints which can restrict the dimensions of the search and can lead to immature convergence.