An enhanced genetic solution for scheduling, module allocation, and binding in VLSI design

This paper presents a novel approach to the high-level synthesis problems of scheduling, module allocation, and module binding for behavioral descriptions. A very general version of this problem is considered where modules may perform different operations in different numbers of control steps. These inherently interdependent problems are solved using an Enhanced Genetic Algorithm (EGA) which is both more robust and more efficient than the simple GA